Supernatural in Shakespeares Macbeth - Witches as Heroines :: GCSE English Literature Coursework

The Witches as the Heroines of Macbeth   Traditionally, the witches of Shakespeares Macbeth have been treated as symbolic manifestations of the potential for evil. Many students and critics of Macbeth enjoy blaming the witches, on with Lady Macbeth, for Macbeths downfall.  Regardless, it may be argued that the witches are the heroines of the play.   One eminent modern literary critic, Terry Eagleton, has addressed the issue of the witches as heroines directly   To any transparent reader--which would seem to exclude Shakespeare himself, his contemporary audiences and almost all literary critics--it is surely clear that positive value in Macbeth lies with the three witches. The witches are the heroines of the piece, however shortsighted the play itself recognizes the fact, and however much the critics may have set out to defame them. (William Shakespeare, p. 2)               For Eagleton, the social reality of the wi tches matters. They are outcasts, much like feminists they live on the fringe of society in a female community, at odds with the male world of civilization, which values military butchery. The fact that they are female and associated with the earthy world beyond the aristocratic oppression in the castles indicates that they are excluded others. Their equality in a female community declares their opposition to the masculine power of the military society. They have no direct power, but they have become expert at manipulating or appealing to the self-destructive contradictions of their military oppressors. They can see Macbeths oddment as a victory of a sort one more viciously individualistic, aggressive male oppressor has gone under.               This suggestion is not entirely full (Eagleton observes that the play does not recognize the issue he is calling attention to), but it underscores a key point in the tragic experience of M acbeth, its connector to a willed repudiation of the deep mysterious heart of life, the place where sexuality and the unconscious hold sway. This aspect of life is commonly associated with and hence symbolized by women, for interwoven reasons which there is not time to go into here (but which would seem to be intimately bound up with womens sexuality and fertility, contacts with the irrational centres of life which men do not understand and commonly fear). In seeking to stamp his own willed vision of the future onto life, the tragic hero rejects a more direct liberty with or acceptance of lifes mystery.

Neurocomputers /article Review :: essays research papers fc

The dream of artificial intelligence that would allow a computer to learn, and thus get really smart, has proven to be something of a incubus so far. That failure has lead biomedical engineer William Ditto and his team of researchers at the Georgia Institute of Technology and Emory University to look beyond silicon and point beyond light chips. Ditto points out that todays processors may be a lot faster, but theyre not a telephone number smarter than they were 40 years ago. Dittos processor is designed with living tissue. The tissue being nerve cells taken from leeches because they argon big, easy to use and they learn quickly. Neurons are able to process images more than a million times faster than the fastest computer (Sincell, 2000). The present review has two purposes (a) to enlighten the reader that the prosecution to build smart computers, microchip engineers look beyond silicon and light to living nerve cells and (b) to suggest that this future technology could be the basi s of the contiguous great computer wave.The article being discussed out of Discover magazine states that brains derive tremendous problem solving abilities from two characteristics of their individual cells. First, a neuron nookie be in any one of thousands of different states, allowing it to store more information that a transistor, which has only two states, on and off (Sincell, 2000). Second, neurons can choose which other neurons to interact with by rearranging their own synaptic connections.Scientists have developed software that attempts to imitate the brains learning process employ only the yes-no binary logic of digital computers with all the connections in a personal computer wired back at the factory. Breaking a adept one of these connections usually crashes the computer.This is not a problem for a neurocomputer Ditto says, because dynamic chaotic systems like these naturally self-organize. An example of this would be the human heart. An obscure heart neuron simply sp arks chaotically, without apparent intelligence. But when it is a part of the neuronal network in a living heart, it synchronizes with all the other neurons to reach a steady heartbeat (Sincell 2000). The neurocomputer would work in a similar way. If a computer programmer posed a problem to a parade of neurons, such as create a regular heartbeat, the neurons would then figure out through trial and error how to rewire their own circuits to produce a steady rhythmic beat.

Self-discovery in Siddhartha Essay -- Hesse Siddhartha Essays

Self-discovery in Siddhartha           Siddhartha, the novel by Hermann Hesse is what can be include as one of the epitomes of allegorical literature.  This wondrous novel is focused on the tribulations of Siddhartha finished his quest for inner peace.    He started out as a schoolgirlish Brahmins son always thirsting for more intellect and perspective in his heart and from there on he endured many transitions.  Siddhartha let himself experience all forms of life in his society.  He unhesitatingly learned more about how different large number lived by stepping into their shoes.  He gained the vast varieties of intellect and perspective that he had longed for through his diversity, and he shrewdly applied it to compose his accurate philosophies of everyday life.           Siddharthas character exemplifies the insatiable feeling that everybody harbors.  He stood for a unity of indi viduals.  He stood for their thirst, and most significantly he stood for their ultimate quench He stood for the insatiable feelings that all people have and need to eventually fill.           As the Brahmins son, Siddhartha could non contain himself.  He was discontent and felt that he had learned all he had to learn amongst his elders, and he was right.  He chose to follow another path in life, a path that would sharpen him another part of how people in his world lived. Siddhartha did not allow himself to stick to something that he could not feel to be right, thus he could not stay and worship the gods his father worshipped. He, as disconte... ...the same time, which all continually         changed and renewed themselves and which were yet all Siddhartha...         He saw the naked bodies of men and women in the postures and         transports of passionate love...He saw all these forms and faces         in a thousand relationships to each other, all helping each other,         loving, hating and destroying each other and become newly born... (p121)           Siddhartha not only experienced them but he overcame them so well that he eventually achieved a great peace inside of him.  He was an example for people to follow through the rigorous course of self discovery.

The Beauty of Dulce et Decorum est Essay -- Dulce et Decorum Est Essay

The Beauty of Dulce et Decorum est Owens terrific use of expression brings the poem Dulce et Decorum Est to life. Vivid mental imagination is prevalent all throughout the poem. His tone is of depression, lack of hope and of course sadness and it reveals his message without writing pages of verse. He accomplishes his message very quickly in the poem, and makes the reader feel like they are actually experiencing what the narrator is going through. Through vivid imagery and compelling metaphors, the poem gives the reader the exact flavor the author wanted. The poem "Dulce et Decorum Est," an anti- struggle poem by Wilfred Owen, makes great use of various poetic skills. This poem is very effective because of its excellent manipulation of the mechanic and emotional parts of poetry. Owens use of exact diction and vivid figurative language emphasizes his point, showing that war is repelling and devastating. Furthermore, the utilization of extremely graphic imagery add s even more to his argument. Through the effective use of all three of these tools, this poem conveys a strong meaning and persuasive argument. The poems use of excellent diction helps to more clearly define what the author is saying. Words like "guttering", "choking", and "drowning" not only show how the man is suffering, but that he is in terrible pain that no human being should endure. Other words like writhing and froth-corrupted say precisely how the man is being tormented. Moreover, the phrase "blood shod" shows how the troops have been on their feet for days, never resting. Also, the fact that the gassed man was "flung" into the wagon reveals the urgency and occupation with fighting. The only thing they ca... ...orum est pro partria mori" means "It is sweet and sightly to die for ones country." Owen calls this a lie by using good diction, vivid comparisons, and graphic images to have the reader feel disgusted at what war is equal to(p) of.he tries to tell us that war is an ugly, brutal and nightmarish business, and not a glorious affair that society seems to beilieve. Most will not have seen the war of Owens experience. But through his vivid words, his gruesome portrayal we know that we do not wish to .Poetry does not have to be pretty, however some poets do not seem to realize this fact. The language chosen in many poems about grisly subjects flows beautifully and elegantly from the page, leaving one feeling little pain about the subject matter of the poem than one really should. What is so beautiful about this poem is its ability to move the reader.

Pushing Kids to the Limit Essay -- Essays Papers

Pushing Kids to the LimitChildren today seem to be involved in many activities outside of school. A number of children whitethorn play soccer, swim, play an instrument, and help out around the house while at the same time trying to succeed academically. In many cases, the broad number of sports played by these children is due to the p arents encouragement, or enforcement. Some parents may enforce after school activities in order to find their children away from the evils of society drugs, alcohol, and simply loitering and causing trouble. Unfortunately, at times, the pressure from the parents can have negative effects on the children academically and/or socially. Some reasons that parents clit their kids so much, could be the small possibility of a college scholarship or money for the child in the future, keeping their children off the street, or the chance that the parents are living vicariously through their childs sports glory.Title IX is a law that requires high schools a nd colleges to give the same amount of money to both girls and boys sports in an attempt to make the genders equal. However, instead of simply equalizing the two sides, this law provides more scholarships for women because they are still competing in fewer sports than men. This go forth usually pourboire the parents interest, making them believe that there are plenty of scholarships out there for their daughters. Unfortunately this is not exactly true. As Lester Munson, an associate editor program at Sports Illustrated, explains it (in an article by Brendan Tierney), This is the theory that many parents have that if they start their child young enough, and work him or her hard enough, that he or she will get a college scholarship or become a professional athle... ...fast. Yes, get children involved in sports and activities. In the long run it will repair off, but please, keep it all in perspective.Works Cited- Shields, David Light. Another View The reality of Olympic dre ams for children. 2002. (November 5, 2002).- Study show athletes among heaviest college drinkers. May 7, 1998. Shawnee News Star. (November 16, 2002).- Tierney, Brendan. How to Become a Better Sports Parent. September 18, 2002. (November 3, 2002).- Vacation or Training Day? Shanghai Parents Shaping Their Children. July 23, 2002. Shanghai Star. (November 17, 2002).

Pushing Kids to the Limit Essay -- Essays Papers

Pushing Kids to the LimitChildren today seem to be involved in many activities outside of school. A identification number of children may lick soccer, swim, play an instrument, and help out around the house while at the same time trying to succeed academically. In many cases, the vast number of sports played by these children is due to the evokes encouragement, or enforcement. Some parents may enforce after school activities in order to keep their children away from the evils of society drugs, alcohol, and simply loitering and causing trouble. Unfortunately, at times, the pressure from the parents can have negative effects on the children academically and/or socially. Some reasons that parents push their kids so much, could be the small possibility of a college scholarship or money for the child in the future, keeping their children off the street, or the chance that the parents are active vicariously through their childs sports glory.Title IX is a law that requires high sc hools and colleges to give the same amount of money to both girls and boys sports in an attempt to make the genders equal. However, instead of simply equalizing the two sides, this law provides more scholarships for women because they are still competing in fewer sports than men. This will usually peak the parents interest, making them believe that there are plenty of scholarships out there for their daughters. Unfortunately this is not exactly true. As Lester Munson, an associate editor at Sports Illustrated, explains it (in an article by Brendan Tierney), This is the theory that many parents have that if they start their child young enough, and work him or her hard enough, that he or she will lodge a college scholarship or become a professional athle... ...fast. Yes, get children involved in sports and activities. In the long run it will pay off, just please, keep it all in perspective.Works Cited- Shields, David Light. Another View The reality of Olympic dreams for chi ldren. 2002. (November 5, 2002).- Study show athletes among heaviest college drinkers. May 7, 1998. Shawnee News Star. (November 16, 2002).- Tierney, Brendan. How to Become a cleanse Sports Parent. September 18, 2002. (November 3, 2002).- Vacation or Training Day? Shanghai Parents Shaping Their Children. July 23, 2002. Shanghai Star. (November 17, 2002).

India shine

As the first reports started to trickle in, the apprehensions of the society workers at 7 RCA and Congress WHQL began to crystallize to gloom. The early trends seemed to be daunting and slowly yet steadily the buildup towards the ultimate forget was emerging. Beyond doubt the flow was In regard of Brutally Kanata Party (BGP), the major opposition party. By the end of the day The Congress was truly humbled with an abysmally low tally 44 seats, the terminal ever in the electoral history since independence. For the first time in two decades BGP emerged on its own as the arrest single party.Backdrop & Introspection The expiry had its impact, at the Congress Parliamentary Board Meeting, the next day. Party President Mrs.. Sonic Gandhi and the Vice President Mr.. Rural Gandhi offered to resign. In an expected twist to the tale, their resignations were not accepted and the party decided to take collective responsibility. There were many issues to focus and It was quite unclear as to ho w the grand old party would anticipate to address these. For two successive terms, the party g all overned at centre along with Its coalition partners. 2004 elections sprang a definite surprise.The BGP government was on an upswing, the campaign was highly Innovative, the thrift In good nick with growth rate around 9. 5% and the image of Its leader Mr.. ABA Payees was most respected. The party perceived itself to be in the impulsive seat and its campaign India Shining was expected to hit off well with the electorate. In spite of strong economic indicators, fairly good record of governance, usual reason of well being and all the predictions of re-election, the BGP were stunned. The congress campaign negated the best of Bops claims thus enabling them to take the lead in forming the government.An intrepid and a politically innovative advertisement blitz failed to impress. The most unexpected happened and Congress secured its victory. The first five years I. E. 2004-2009, were conse rvative yet relatively non controversial. The symptoms of decline began to emerge. The economy was presentation signs of recession, growth rate was on the slump, global economic conditions started to show challenging signs yet the congress managed to pull off In 2009 to get re-elected to form a coalition government again. Things began to change Emboldened by its re-election and the arty started to influence policies and decisions resulting in an indifferent state of governance, conflicts, dilution of control, ineffective monitoring. Ministries began to exert themselves and more or less(prenominal) operated with impunity and became non responsive to PM abundance of corrupt practices and scandals broke out, economy began to decline with high inflation, price rise, unemployment, dropping investments, growing incidents of violence against women, Look pal turbulence etc which put a great deal of pressure.The PM to large extent restricted his operations to his domain and as not seen ex erting himself to bring the administration infra his control. Coupled with this was the most Ineffective approach towards media and interaction on media which left the party scuttling for cover on many occasions. There emerged a general sense of strolls and stagnant state of affairs which was becoming a common overlook a large number of them. For the elections 2014, the focus of the party remained on personality I. E.BGP preliminaries medical prognosis and the issue of secularism. The emergence of PAP and its impact was sidelined and many issues relevant to the runner context of elections were not taken into cognizance. The approach was quite ambivalent and ambiguous. The net result was a mixed message to the electorate looking for answers to questions which remained unanswered. Campaign Challenges BGP By 2011, the BGP apparently began its preparations to target the 2014 elections. The party began its preparation with a focus on identifying the correct strategy.Having face up th e double defeat in 2004 and 2009, it realized that success is accomplishable if the party is sufficient to project its image and be identified as a matter alternative. In order to do so, it had to set itself on important issues namely Personality, Platform, Plank, Diversity, Demography and Development. By default, the give birth lead government seemed to pave way for watch crystal of BGP campaign strategy by series of actions and inactions. As a first step, the BGP began the exercise of identifying a candidate suitable for spearheading the campaign.After a series of ups and towns, the party was able to narrow down to the CM of Gujarat, Mr.. Neared Mood. The choice was fraught with controversy as many including leading political analysts felt that this old endanger the Bops chances. Even at bottom the party there was a dissent from senior leaders akin ELK Divan, Cushman Swarms etc. The other national parties seemed to rejoice as they felt it was a trap BGP had set for itself a nd the choice would undo their chances. By 2013, the official declaration took place and Mood was anointed the PM candidate, the face of BGP for the 2014 elections.While the choice of Mood was becoming a controversy, the approach towards elections was to be aligned. The reach out was tremendous, the political alliances were challenging, the geographical ileitis were imposing, the regional heavy weights were difficult to rope in, Mood as a choice was overly alienating some erstwhile partners like JDK(U). The party had to identify themes and means to reach out to the target population and make a convincing pitch. The surround had undergone an extensive technology makeover since 2004.The decade has brought in changes in perception as regards elections, greater apolitical pro activity and dependence on reliable and immobile communications. The demography too has undergone a rapid change there was a growing sense of discontent on account of various factors effecting the society, econom y and evildoer. Moreover, issues like unrest due to nationalism, cross border tensions, reactive neighborhood etc also tended to make the Job of convincing electorate that much more difficult. Though the national bureau on multiple fronts was grim, the fact that such a situation was a hidden opportunity or not was truly debatable.There were challenges in abundance and the choices were bound by time. The objective was to conceive and present a campaign which appeals to all sections simultaneously and converts the message to conviction and thereafter to action in terms of vote. starting time a campaign too early would be self defeating (2004 stands testimony for that) and too late would be ineffective or defensive (2009 a possible example). The question of when, where, how and who? For an effective campaign message and medium were to be identified and reinforced convincingly as a national alternative.Bops approach towards 2014 was characterized by incorporate planning and focused execution. It SE about the Job in a clinical manner with pre defined objectives to achieve. The campaign activity was set in question by basic reorganization of the party dare, revamp & election of national executive, short listing prospective Prime Ministerial candidate, identifying issues relevant for campaign, projecting party agenda through articulate spokespersons, adopting multiple media options to leverage reach & communicate were part of numerous hurdles that needed to be considered.The national demography has undergone a substantial change and the increased awareness would also need specific attention. The climate across the country appears to have undergone a change with people across the cross section of society evincing a new found interest in elections. The youth and the educated middle and the upper middle class know for its disregard towards participation in elections appeared reengineering. A conservative estimate put the number of youth vote bank across the countr y was at carbon million.A substantial chunk of this needed to be harnessed and it was also essential for the party to enhance its vote share across the country. The party needed to right identify the challenges of multiple segments divided by diverse parameters such as culture, language, education, age, economic status, religion. Large number of local issues were taking precedence over sectional issues which diluted the partys influence visavisa the regional players.The campaign called for deliberate action plan with defined objectives and with red flags across the time span to animate/ decelerate the campaign. The party decided to go all out leveraging the best of technical brains. A multi-tiered campaign was to be conducted with the objective of targeting and winning over the circumspect population in its favor.. The impact was like a corporate entity trying to rebind itself with a new carrefour launch. There was branding, there was product development, here was segment speci fic media strategy and there was people to people contact.As the stage was set, the BGP was in top gear with the assemble results in northern states showing a thundering favor towards BGP. The time of opportune but the choices were different and difficult. Any misalignment would prove costly. The Aftermath The campaign was highly intense. Both the national parties sky in all the resources. It was a no holds bar election with reputations at stake. The results were historic. An outright majority for BGP and an irrevocable domination of the Look Saba long with its allies.The congress and the PUP stood decimated. The results were a surprise & beyond all the expectations of all political parties, experts and election surveys. What went right for BGP and why? What factors in this election are lessons for use of effective publicize and media promotion? How did Congress Fail to sense the pulse? What went wrong with experts and pollsters who could not identify the mood? Did Personality, Pl atform, Plank, Diversity, Demography and Development influence the advertising and media choices and if so how?

Attendance System

Student attention governance Based On fingermark Recognition and One-to-Many co-ordinated A thesis submitted in incomplete ful? llment of the requirements for the degree of Bachelor of Computer Application in Computer knowledge by Sachin (Roll no. 107cs016) and Arun Sharma (Roll no. 107cs015) Under the guidance of Prof. R. C. Tripathi Department of Computer knowledge and Engineering National Institute of Technology Rourkela Rourkela-769 008, Orissa, India 2 . Dedicated to Our Pargonnts and Indian Scienti? c Community . 3 National Institute of Technology Rourkela Certi? cateThis is to certify that the trade union movement entitled, Student Attendance System Based On fingermark Recognition and One-to-Many twin(a) submitted by Rishabh Mishra and Prashant Trivedi is an authentic work carried out(p) by them under my dir electroshock therapyion and guidance for the partial ful? llment of the requirements for the award of Bachelor of Technology Degree in Computer Science and Engine ering at National Institute of Technology, Rourkela. To the best of my knowledge, the amour embodied in the project has non been submitted to any other University / Institute for the award of any Degree or Diploma.Date 9/5/2011 Rourkela (Prof. B. Majhi) Dept. of Computer Science and Engineering 4 Abstract Our project aims at purposeing an scholar attendance remains which could e? ectively manage attendance of students at institutes like NIT Rourkela. Attendance is marked after student identi? cation. For student identi? cation, a ? ngerprint recognition build identi? cation frame is practiced. reproduces be considered to be the best and quick method for biometric identi? cation. They ar skilful to spend, unique for ein truth person and does not change in unrivaleds lifetime. Fingerprint recognition is a mature ? ld today, still still identifying soulfulness from a set of enrolled ? ngerprints is a time taking action. It was our responsibility to improve the ? nger print identi? cation frame for implementation on rangy-mouthed databases e. g. of an institute or a sphere and so forth In this project, many new algorithms take for been utilise e. g. gender estimation, key based one to many twinned, removing demarcation minutiae. victimisation these new algorithms, we excite developed an identi? cation system which is faster in implementation than any other unattached today in the market. Although we be using this ? ngerprint identi? cation system for student identi? ation advise in our project, the coordinated results ar so good that it could perform very well on large databases like that of a country like India (MNIC Project). This system was implemented in Matlab10, Intel Core2Duo processor and comparison of our one to many identi? cation was through with existing identi? cation proficiency i. e. one to one identi? cation on same platform. Our duplicate proficiency runs in O(n+N) time as compared to the existing O(Nn2 ). The ? n gerprint identi? cation system was tested on FVC2004 and Veri? nger databases. 5 Acknowledgments We express our profound gratitude and indebtedness to Prof. B.Majhi, Department of Computer Science and Engineering, NIT, Rourkela for introducing the consecrate topic and for their inspiring intellectual guidance, constructive criticism and valuable suggestion throughout the project work. We are also thankful to Prof. Pankaj Kumar Sa , Ms. Hunny Mehrotra and other sta? s in Department of Computer Science and Engineering for motivating us in improving the algorithms. Fin all in ally we would like to thank our parents for their support and permitting us stay for more days to complete this project. Date 9/5/2011 Rourkela Rishabh Mishra Prashant Trivedi Contents 1 Introduction 1. 1 1. 2 1. 3 1. 4 1. 1. 6 1. 7 chore Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . Motivation and Challenges . . . . . . . . . . . . . . . . . . . . . . . . Using Biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What is ? ngerprint? . . . . . . . . . . . . . . . . . . . . . . . . . . . Why use ? ngerprints? . . . . . . . . . . . . . . . . . . . . . . . . . . . Using ? ngerprint recognition system for attendance management . . . Organization of the thesis . . . . . . . . . . . . . . . . . . . . . . . . 17 17 17 18 18 19 19 19 21 21 22 23 24 24 30 30 33 33 33 35 35 36 36 2 Attendance Management Framework 2. 2. 2 2. 3 2. 4 2. 5 ironware computer software take Design . . . . . . . . . . . . . . . . . . . . Attendance Management Approach . . . . . . . . . . . . . . . . . . . On-Line Attendance Report Generation . . . . . . . . . . . . . . . . . Ne iirk and Database Management . . . . . . . . . . . . . . . . . . Using wireless net profit instead of local area network and bringing portability . . . 2. 5. 1 2. 6 Using Por carry over Device . . . . . . . . . . . . . . . . . . . . . . Comparison with other student attendance systems . . . . . . . . . . 3 Fingerprint Identi? cation System 3. 1 3. 2 How Fingerprint Recognition ample treatment? . . . . . . . . . . . . . . . . . Fingerprint Identi? cation System Flowchart . . . . . . . . . . . . . . 4 Fingerprint Enhancement 4. 1 4. 2 4. 3 divider . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Orientation estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 6 CONTENTS 4. 4 4. 5 4. 6 4. 7 Ridge frequency union . . . . . . . . . . . . . . . . . . . . . . . Gabor ? lter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Binarisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thinning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 38 39 40 40 41 41 42 42 43 44 45 45 45 46 47 47 50 51 53 53 54 54 55 56 57 59 59 59 59 60 5 Feature decline 5. 1 5. 2 Finding the address Point . . . . . . . . . . . . . . . . . . . . . . . Minutiae Extr action and Post-Processing . . . . . . . . . . . . . . . . 5. 2. 1 5. 2. 2 5. 2. 3 5. 3 Minutiae Extraction . . . . . . . . . . . . . . . . . . . . . . . Post-Processing . . . . . . . . . . . . . . . . . . . . . . . . . Removing Boundary Minutiae . . . . . . . . . . . . . . . . . . Extraction of the key . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 3. 1 What is key? . . . . . . . . . . . . . . . . . . . . . . . . . . Simple refer . . . . . . . . . . . . . . . . . . . . . . . . . . . . Complex Key . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 division of Database 6. 1 6. 2 6. 3 Gender theme . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classi? cation of Fingerprint . . . . . . . . . . . . . . . . . . . . . . . Partitioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Matching 7. 1 7. 2 7. 3 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Existing Matching Techniques . . . . . . . . . . . . . . . . . . . . . One to Many twin(a) . . . . . . . . . . . . . . . . . . . . . . . . . . 7. 3. 1 7. 4 7. 5 regularity of One to Many Matching . . . . . . . . . . . . . . . Performing key take on and full interconnected . . . . . . . . . . . . . . . . beat Complexity of this matching technique . . . . . . . . . . . . . . 8 data-based Analysis 8. 1 8. 2 carrying into action Environment . . . . . . . . . . . . . . . . . . . . . . Fingerprint Enhancement . . . . . . . . . . . . . . . . . . . . . . . . 8. 2. 1 8. 2. 2 Segmentation and Normalization . . . . . . . . . . . . . . . . Orientation Estimation . . . . . . . . . . . . . . . . . . . . . . 8 8. 2. 3 8. 2. 4 8. . 5 8. 3 CONTENTS Ridge Frequency Estimation . . . . . . . . . . . . . . . . . . . Gabor Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . Binarisation and Thinning . . . . . . . . . . . . . . . . . . . . 60 60 61 62 62 62 63 64 64 64 64 65 66 66 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 3. 1 Minutiae Extraction and Post Processing . . . . . . . . . . . . Minutiae Extraction . . . . . . . . . . . . . . . . . . . . . . . by and by Removing Spurious and Boundary Minutiae . . . . . . . 8. 3. 2 Reference Point sensing . . . . . . . . . . . . . . . . . . . . 8. 4 Gender Estimation and Classi? ation . . . . . . . . . . . . . . . . . . 8. 4. 1 8. 4. 2 Gender Estimation . . . . . . . . . . . . . . . . . . . . . . . . Classi? cation . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 5 8. 6 Enrolling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 6. 1 8. 6. 2 Fingerprint Veri? cation Results . . . . . . . . . . . . . . . . . Identi? cation Results and Comparison with Other Matching techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 70 73 74 75 75 79 8. 7 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Conclusion 9. 1 Outcomes of this Project . . . . . . . . . . . . . . . . . . . . . . . . . 10 Future Work and Expectations 10. 1 Approach for Future Work A Matlab functions . . . . . . . . . . . . . . . . . . . . . . . lean of grades 1. 1 2. 1 2. 2 2. 3 2. 4 2. 5 2. 6 2. 7 2. 8 3. 1 4. 1 4. 2 Example of a ridgepolelineline ending and a bifurcation . . . . . . . . . . . . . . Hardware present in classrooms . . . . . . . . . . . . . . . . . . . . . Classroom Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . Network Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ER Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 0 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 1 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 2 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Por hold over Device . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fingerprint Identi? cation S ystem Flowchart . . . . . . . . . . . . . . Orientation Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . (a)Original Image, (b)Enhanced Image, (c)Binarised Image, (d) change state Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 1 course 1 ? lter response c1k , k = 3, 2, and 1. Row 2 ? lter response c2k , k = 3, 2, and 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 2 5. 3 Examples of (a)ridge-ending (CN=1) and (b)bifurcation picture element (CN=3) 42 43 40 18 22 23 25 26 27 27 28 29 34 37 Examples of typical assumed minutiae organizes (a)Spur, (b)Hole, (c)Triangle, (d)Spike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 44 44 45 48 5. 4 5. 5 5. 6 6. 1 Skeleton of window concentrate on at boundary minutiae . . . . . . . . . . Matrix Representation of boundary minutiae . . . . . . . . . . . . . Key Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gender Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 10 6. 2 6. 3 LIST OF FIGURES 135o thrusts of a ? ngerprint . . . . . . . . . . . . . . . . . . . . . . . . Fingerprint Classes (a)Left Loop, (b)Right Loop, (c)Whorl, (d1)Arch, (d2)Tented Arch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. 4 7. 1 8. 1 8. 2 8. 3 8. 4 8. 5 8. 6 8. 7 8. 8 8. 9 Partitioning Database . . . . . . . . . . . . . . . . . . . . . . . . . . One to Many Matching . . . . . . . . . . . . . . . . . . . . . . . . . . Normalized Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . Orientation Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ridge Frequency Image . . . . . . . . . . . . . . . . . . . . . . . . . . Left-Original Image, Right-Enhanced Image . . . . . . . . . . . . . . Binarised Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . slashed Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . All Extracted Minutiae . . . . . . . . . . . . . . . . . . . . . . . . . . Composite Image with spurious and boundary minutiae . . . . . . . . Minutiae Image after post- affect . . . . . . . . . . . . . . . . . 51 52 57 59 60 60 61 61 62 62 63 63 64 65 50 8. 10 Composite Image after post-processing . . . . . . . . . . . . . . . . . 8. 11 Plotted Minutiae with Reference Point(Black Spot) . . . . . . . . . . 8. 12 Graph era taken for Identi? cation vs Size of Database(key based one to many identi? cation) . . . . . . . . . . . . . . . . . . . . . . . . 8. 13 Graph clock time taken for Identi? cation vs Size of Database (n2 identi? cation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 14 evaluate Graph for comparison Time taken for Identi? cation vs Size of Database(1 million) . . . . . . . . . . . . . . . . . . . . . . . . . 68 69 71 List of evades 2. 1 5. 1 8. 1 8. 2 8. 3 8. 4 8. 5 8. 6 8. 7 8. 8 Estimated Budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Properties of Crossing Number . . . . . . . . . . . . . . . . . . . . . 22 43 64 65 66 66 67 67 68 Average Number of Minutiae forward and after post-processing . . . . Ridge parsimoniousness Calculation Results . . . . . . . . . . . . . . . . . . . . Classi? cation Results on Original Image . . . . . . . . . . . . . . . . Classi? cation Results on Enhanced Image . . . . . . . . . . . . . . . Time taken for Classi? cation . . . . . . . . . . . . . . . . . . . . . . .Time taken for Enrolling . . . . . . . . . . . . . . . . . . . . . . . . . Error Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Performance of ours and n2 matching based identi? cation techniques on a database of size 150 . . . . . . . . . . . . . . . . . . . . . . . . . 70 11 List of algorithmic rules 1 2 3 4 Key Extraction Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . Gender Estimation Algorithm . . . . . . . . . . . . . . . . . . . . . . . Key Based One to Many Matching Algorithm . . . . . . . . . . . . . . Matc hing Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 49 55 56 12Chapter 1 Introduction 1. 1 Problem Statement Designing a student attendance management system based on ? ngerprint recognition and faster one to many identi? cation that manages records for attendance in institutes like NIT Rourkela. 1. 2 Motivation and Challenges Every musical arrangement whether it be an educational institution or business organization, it has to maintain a proper record of attendance of students or employees for e? ective functioning of organization. Designing a better attendance management system for students so that records be maintained with ease and true statement was an important key behind motivating this project.This would improve accuracy of attendance records because it pass on remove all the hassles of roll wishing and testament save valuable time of the students as well as instructors. Image processing and ? ngerprint recognition are very advanced today in terms of technology. It was our responsibility to improve ? ngerprint identi? cation system. We decreased matching time by partitioning the database to tenth and improved matching using key based one to many matching. 13 14 CHAPTER 1. INTRODUCTION 1. 3 Using Biometrics Biometric Identi? cation Systems are wide used for unique identi? cation of humans mainly for veri? cation and identi? ation. Biometrics is used as a form of identity access management and access control. So use of biometrics in student attendance management system is a secure approach. There are many display cases of biometric systems like ? ngerprint recognition, face recognition, voice recognition, glad recognition, palm recognition etc. In this project, we used ? ngerprint recognition system. 1. 4 What is ? ngerprint? A ? ngerprint is the pattern of ridges and valleys on the surface of a ? ngertip. The end tear downs and crossing blames of ridges are conjureed minutiae. It is a widely accepted assumption that the minutiae pattern of severally ? ger is unique and does not change during ones life. Ridge endings are the points where the ridge curve terminates, and bifurcations are where a ridge splits from a single path to two paths at a Y-junction. variety 1 illustrates an example of a ridge ending and a bifurcation. In this example, the black pels correspond to the ridges, and the white pixels correspond to the valleys. normal 1. 1 Example of a ridge ending and a bifurcation When human ? ngerprint experts determine if two ? ngerprints are from the same ? nger, the matching degree between two minutiae pattern is one of the virtually important factors.Thanks to the similarity to the way of human ? ngerprint experts and compactness of templates, the minutiae-based matching method is the most widely studied matching method. 1. 5. WHY USE FINGERPRINTS? 15 1. 5 Why use ? ngerprints? Fingerprints are considered to be the best and fastest method for biometric identi? cation. They are secure to u se, unique for every person and does not change in ones lifetime. Besides these, implementation of ? ngerprint recognition system is cheap, easy and accurate up to satis? ability. Fingerprint recognition has been widely used in both(prenominal) forensic and civilian applications.Compared with other biometrics cavorts , ? ngerprint-based biometrics is the most proven technique and has the largest market shares . Not only it is faster than other techniques but also the energy consumption by such systems is too less. 1. 6 Using ? ngerprint recognition system for attendance management Managing attendance records of students of an institute is a tedious task. It consumes time and paper both. To make all the attendance connect work automatic and on-line, we swallow designed an attendance management system which could be implemented in NIT Rourkela.It uses a ? ngerprint identi? cation system developed in this project. This ? ngerprint identi? cation system uses existing as well as new techniques in ? ngerprint recognition and matching. A new one to many matching algorithm for large databases has been introduced in this identi? cation system. 1. 7 Organization of the thesis This thesis has been organized into ten chapters. Chapter 1 introduces with our project. Chapter 2 explains the proposed design of attendance management system. Chapter 3 explains the ? ngerprint identi? cation system used in this project.Chapter 4 explains upraisement techniques, Chapter 5 explains feature extraction methods, Chapter 6 explains our database partitioning approach . Chapter 7 explains matching technique. Chapter 8 explains experimental work do and executing analysis. Chapter 9 includes conclusions and Chapter 10 introduces proposed future work. Chapter 2 Attendance Management Framework Manual attendance taking and floor generation has its readyations. It is well enough for 30-60 students but when it comes to taking attendance of students large in calculate, it is di? cult. For taking attendance for a lecture, a conference, etc. oll calling and manual attendance system is a failure. Time waste over responses of students, waste of paper etc. are the disadvantages of manual attendance system. Moreover, the attendance spread over is also not generated on time. Attendance report which is circulated over NITR webmail is two months old. To repress these non-optimal situations, it is necessary that we should use an automatic on-line attendance management system. So we present an implementable attendance management framework. Student attendance system framework is divided into three parts Hardware/Software Design, Attendance Management Approach and On-line Report Generation. apiece of these is explained below. 2. 1 Hardware Software Level Design Required hardware used should be easy to maintain, implement and easily available. Proposed hardware consists following parts (1)Fingerprint S deposener, (2)LCD/Display Module (optional), (3)Computer 16 2. 2. atte ndance MANAGEMENT APPROACH Table 2. 1 Estimated Budget Device Cost of Number of Total Name One unit of measurement Units Required Unit Budget S seatner 500 100 50000 PC 21000 100 2100000 Total 21,50,000 (4)LAN connection 17 Fingerprint s disregardner give be used to arousal ? ngerprint of t from each oneers/students into the computer software.LCD display give be displaying rolls of those whose attendance is marked. Computer Software impart be interfacing ? ngerprint electronic s squirtner and LCD and will be connected to the network. It will input ? ngerprint, will process it and extract features for matching. After matching, it will update database attendance records of the students. Figure 2. 1 Hardware present in classrooms Estimated Budget Estimated personify of the hardware for implementation of this system is sh protest in the table 2. 1. Total number of classrooms in NIT Rourkela is around 100. So number of units required will be 100. 2. 2 Attendance Management Approach This part explains how students and teachers will use this attendance management system. Following points will make sure that attendance is marked correctly, without any chore (1)All the hardware will be inside classroom. So outside interference will be absent. (2)To remove unauthorized access and unwanted attempt to deject the hardware by students, all the hardware except ? ngerprint s hindquartersner could be put inside a small 18 CHAPTER 2. ATTENDANCE MANAGEMENT framework cabin. As an alternate solution, we can install CCTV cameras to prevent unprivileged activities. (3)When teacher enters the classroom, the attendance cross will start.Computer software will start the process after inputting ? ngerprint of teacher. It will ? nd the Subject ID, and Current Semester using the ID of the teacher or could be set manually on the software. If teacher doesnt enter classroom, attendance marking will not start. (4)After some time, say 20 minutes of this process, no attendance will be g iven because of late entrance. This time period can be increased or decreased as per requirements. Figure 2. 2 Classroom Scenario 2. 3 On-Line Attendance Report Generation Database for attendance would be a table having following ? elds as a combination for primary ? ld (1)Day,(2)Roll,(3)Subject and following non-primary ? elds (1)Attendance,(2)Semester. Using this table, all the attendance can be managed for a student. For on-line report generation, a simple website can be hosted on NIT Rourkela servers, 2. 4. NETWORK AND DATABASE MANAGEMENT 19 which will access this table for showing attendance of students. The sql queries will be used for report generation. Following ask will give innate numbers of classes held in subject CS423 SELECT COUNT(DISTINCT Day) FROM AttendanceTable WHERE SUBJECT = CS423 AND Attendance = 1 For attendance of oll 107CS016, against this subject, following query will be used SELECT COUNT(Day) FROM AttendanceTable WHERE Roll = 107CS016 AND SUBJECT = CS423 A ND Attendance = 1 Now the attendance percent can easily be calculated ClassesAttended ? 100 ClassesHeld Attendance = (2. 1) 2. 4 Network and Database Management This attendance system will be spread over a wide network from classrooms via intranet to internet. Network diagram is shown in ? g. 2. 3. Using this network, attendance reports will be made available over internet and e-mail. A monthly report will be sent to each student via email and website will show the updated attendance.Entity relationship diagram for database of students and attendance records is shown in ? g. 2. 4. In ER diagram, primary ? elds are Roll, Date, SubjectID and TeacherID. Four tables are Student, Attendance, Subject and Teacher. Using this database, attendance could easily be maintained for students. Data? ow is shown in data ? ow diagrams (DFD) shown in ? gures 2. 5, 2. 6 and 2. 7. 2. 5 Using wireless network instead of LAN and bringing portability We are using LAN for communication among servers and h ardwares in the classrooms. We can instead use wireless LAN with takeout thingmajigs.Portable device will have an embedded ? ngerprint scanner, wireless connection, a microprocessor loaded with a software, memory and a display terminal, chequer ? gure 2. 5. Size of device could be small like a mobile phone depending upon how well the device is manufactured. 20 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK Figure 2. 3 Network Diagram 2. 5. USING WIRELESS NETWORK INSTEAD OF LAN AND BRINGING PORTABILITY21 Figure 2. 4 ER Diagram 22 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK Figure 2. 5 Level 0 DFD Figure 2. 6 Level 1 DFD 2. 5. USING WIRELESS NETWORK INSTEAD OF LAN AND BRINGING PORTABILITY23 Figure 2. Level 2 DFD 24 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK This device should have a wireless connection. Using this wireless connection, Figure 2. 8 Portable Device attendance taken would be updated automatically when device is in network of the nodes which are storing the attendance recor ds. Database of enrolled ? ngerprints will be in this portable device. Size of enrolled database was 12. 1 MB when 150 ? ngerprints were enrolled in this project. So for 10000 students, atleast 807 MB or more space would be required for storing enrolled database. For this purpose, a removable memory chip could be used.We cannot use wireless LAN here because fetching data using wireless LAN will not be possible because of less range of wireless devices. So enrolled data would be on chip itself. Attendance results will be updated when portable device will be in the range of nodes which are storing attendance reports. We may update all the records online via the mobile network provided by di? erent companies. Today 3G network provides su? cient throughput which can be used for updating attendance records automatically without going near nodes. In such case, 2. 6. COMPARISON WITH OTHER STUDENT ATTENDANCE SYSTEMS 25 he need of database inside memory chip will not be mandatory. It will be fetched by using 3G mobile network from central database repository. The design of such a portable device is the task of embedded system engineers. 2. 5. 1 Using Portable Device In this section, we suggest the working of portable device and the method of using it for marking attendance. The device may either be having touchscreen input/display or buttons with lcd display. A software oddly designed for the device will be running on it. Teachers will verify his/her ? ngerprint on the device before adult it to students for marking attendance.After verifying the teachers identity, software will ask for course and and other required information about the class which he or she is going to teach. Software will ask teacher the time after which device will not mark any attendance. This time can vary depending on the teachers mood but our suggested set is 25 minutes. This is done to prevent late entrance of students. This step will hardly take few seconds. Then students will be given devi ce for their ? ngerprint identi? cation and attendance marking. In the continuation, teacher will start his/her lecture.Students will hand over the device to other students whose attendance is not marked. After 25 minutes or the time decided by teacher, device will not input any attendance. After the class is over, teacher will take device and will end the lecture. The main function of software running on the device will be ? ngerprint identi? cation of students followed by report generation and sending reports to servers using 3G network. Other functions will be downloading and updating the database available on the device from central database repository. 2. 6 Comparison with other student attendance systemsThere are various other kind of student attendance management systems available like RFID based student attendance system and GSM-GPRS based student attendance system. These systems have their own pros and cons. Our system is better because ? rst it saves time that could be use d for teaching. Second is portability. Portability 26 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK has its own advantage because the device could be taken to any class wherever it is scheduled. While GSM-GPRS based systems use lieu of class for attendance marking which is not dynamic and if schedule or berth of the class changes, wrong attendance might be marked.Problem with RFID based systems is that students have to carry RFID cards and also the RFID detectors are needed to be installed. Nonetheless, students may give proxies easily using friends RFID card. These problems are not in our system. We used ? ngerprints as recognition criteria so proxies cannot be given. If portable devices are used, attendance marking will be done at any place and any time. So our student attendance system is far better to be implemented at NITR. Chapter 3 Fingerprint Identi? cation System An identi? cation system is one which helps in identifying an individual among any mess when detailed informatio n is not available. It may involve matching available features of candidate like ? ngerprints with those already enrolled in database. 3. 1 How Fingerprint Recognition works? Fingerprint range of mountainss that are found or scanned are not of optimum quality. So we remove noises and enhance their quality. We extract features like minutiae and others for matching. If the sets of minutiae are matched with those in the database, we call it an identi? ed ? ngerprint. After matching, we perform post-matching steps which may include showing details of identi? ed candidate, marking attendance etc.A brief ? owchart is shown in close section. 3. 2 Fingerprint Identi? cation System Flowchart A brief methodology of our Fingerprint Identi? cation System is shown here in following ? owchart. Each of these are explained in the later chapters. 27 28 CHAPTER 3. FINGERPRINT IDENTIFICATION SYSTEM Figure 3. 1 Fingerprint Identi? cation System Flowchart Chapter 4 Fingerprint Enhancement The scene a cquired from scanner is sometimes not of perfect quality . It gets corrupted due to irregularities and non-uniformity in the impression taken and due to variations in the skin and the presence of the scars, humidity, irt etc. To overcome these problems , to reduce noise and enhance the de? nition of ridges against valleys, various techniques are applied as following. 4. 1 Segmentation Image segmentation 1 separates the cotton up regions and the scope regions in the insure. The foreground regions refers to the clear ? ngerprint area which contains the ridges and valleys. This is the area of interest. The background regions refers to the regions which is outside the borders of the main ? ngerprint area, which does not contain any important or valid ? ngerprint information.The extraction of noisy and misguided minutiae can be done by applying minutiae extraction algorithm to the background regions of the ikon. Thus, segmentation is a process by which we can discard these backgroun d regions, which results in more reliable extraction of minutiae points. We are going to use a method based on variability thresholding . The background regions exhibit a very low grey scale variableness value , whereas the foreground regions have a very high variance . Firstly , the name is divided into blocks and the grey-scale variance is calculated for each block in the image .If the variance is less than the global threshold , then the block is assigned to be part of background region or else 29 30 CHAPTER 4. FINGERPRINT ENHANCEMENT it is part of foreground . The grey level variance for a block of size S x S can be calculated as 1 V ar(k) = 2 S S? 1 S? 1 (G(i, j) ? M (k))2 i=0 j=0 (4. 1) where Var(k) is the grey level variance for the block k , G(i,j) is the grey level value at pixel (i,j) , and M(k) denotes the basal grey level value for the corresponding block k . 4. 2 Normalization Image normalization is the next step in ? ngerprint enhancement process.Normalizatio n 1 is a process of standardizing the intensity determine in an image so that these intensity values lies in spite of appearance a certain desired range. It can be done by adjusting the range of grey-level values in the image. Let G(i, j) denotes the grey-level value at pixel (i, j), and N(i, j) represent the normalized grey-level value at pixel (i, j). Then the normalized image can de? ned as ? ? M + 0 N (i, j) = ? M ? 0 V0 (G(i,j)? M )2 V V0 (G(i,j)? M )2 V , if I(i, j) M , otherwise where M0 and V0 are the estimated sozzled and variance of I(i, j), respectively . 4. 3 Orientation estimation The orientation ? eld of a ? ngerprint image de? es the local orientation of the ridges contained in the ? ngerprint . The orientation estimation is a fundamental step in the enhancement process as the subsequent Gabor ? ltering stage relies on the local orientation in order to e? ectively enhance the ? ngerprint image. The least mean square estimation method used by Raymond Thai 1 is use d to compute the orientation image. However, instead of estimating the orientation block-wise, we have chosen to extend their method into a pixel-wise scheme, which produces a ? ner and more accurate estimation of the orientation ? eld. The steps for calculating the orientation at pixel i, j) are as follows 4. 3. ORIENTATION ESTIMATION 31 1. Firstly , a block of size W x W is concentrate on at pixel (i, j) in the normalized ? ngerprint image. 2. For each pixel in the block, compute the gradients dx (i, j) and dy (i, j), which are the gradient magnitudes in the x and y directions, respectively. The horizontal Sobel operator6 is used to compute dx(i, j) 1 0 -1 2 0 -21 0 -1 Figure 4. 1 Orientation Estimation 3. The local orientation at pixel (i j) can then be estimated using the following equations i+ W 2 j+ W 2 Vx (i, j) = u=i? W 2 i+ W 2 v=j? W 2 j+ W 2 2? x (u, v)? y (u, v) (4. 2) Vy (i, j) = u=i? W v=j? W 2 2 2 2 ? (u, v) ? ?y (u, v), (4. 3) ?(i, j) = 1 Vy (i, j) tan? 1 , 2 Vx ( i, j) (4. 4) where ? (i, j) is the least square estimate of the local orientation at the block refer at pixel (i, j). 4. Smooth the orientation ? eld in a local neighborhood using a Gaussian ? lter. The orientation image is ? rstly converted into a continuous vector ? eld, which is de? ned as ? x (i, j) = cos 2? (i, j), ? y (i, j) = sin 2? (i, j), (4. 5) (4. 6) where ? x and ? y are the x and y components of the vector ? eld, respectively. After 32 CHAPTER 4. FINGERPRINT ENHANCEMENT the vector ? eld has been computed, Gaussian smoothing is then performed as follows w? w? 2 ?x (i, j) = w? u=? 2 w? v=? 2 G(u, v)? x (i ? uw, j ? vw), (4. 7) w? 2 w? 2 ?y (i, j) = w? u=? 2 w? v=? 2 G(u, v)? y (i ? uw, j ? vw), (4. 8) where G is a Gaussian low-pass ? lter of size w? x w? . 5. The ? nal smoothed orientation ? eld O at pixel (i, j) is de? ned as O(i, j) = ? y (i, j) 1 tan? 1 2 ? x (i, j) (4. 9) 4. 4 Ridge Frequency Estimation Another important parameter,in addition to the orientation image , that can be used in the construction of the Gabor ? lter is the local ridge frequency. The local frequency of the ridges in a ? ngerprint is represented by the frequency image. The ? st step is to divide the image into blocks of size W x W. In the next step we project the greylevel values of each pixels located inside each block along a direction perpendicular to the local ridge orientation. This projection results in an almost sinusoidal-shape wave with the local minimum points denoting the ridges in the ? ngerprint. It involves smoothing the projected waveform using a Gaussian lowpass ? lter of size W x W which helps in reducing the e? ect of noise in the projection. The ridge spacing S(i, j) is then calculated by counting the median number of pixels between the consecutive minima points in the projected waveform.The ridge frequency F(i, j) for a block centered at pixel (i, j) is de? ned as F (i, j) = 1 S(i, j) (4. 10) 4. 5. GABOR FILTER 33 4. 5 Gabor ? lter Gabor ? lters 1 are used because they have orientation-selective and frequencyselective properties. Gabor ? lters are called the mother of all other ? lters as other ? lter can be derived using this ? lter. Therefore, applying a properly tuned Gabor ? lter can preserve the ridge structures epoch reducing noise. An even-symmetric Gabor ? lter in the spatial domain is de? ned as 1 x2 y2 G(x, y, ? , f ) = exp? ? + ? cos 2? f x? , 2 2 2 ? x ? y (4. 11) x? = x cos ? + y sin ? , (4. 12) y? ? x sin ? + y cos ? , (4. 13) where ? is the orientation of the Gabor ? lter, f is the frequency of the cosine wave, ? x and ? y are the standard deviations of the Gaussian envelope along the x and y axes, respectively, and x? and y? de? ne the x and y axes of the ? lter aline frame respectively. The Gabor Filter is applied to the ? ngerprint image by spatially convolving the image with the ? lter. The convolution of a pixel (i,j) in the image requires the corresponding orientation value O(i,j) and the ridge frequenc y value F(i,j) of that pixel . wy 2 wx 2 E(i, j) = u=? wx 2 w v=? 2y G(u, v, O(i, j), F (i, j))N (i ? u, j ? v), (4. 4) where O is the orientation image, F is the ridge frequency image, N is the normalized ? ngerprint image, and wx and wy are the width and height of the Gabor ? lter mask, respectively. 34 CHAPTER 4. FINGERPRINT ENHANCEMENT 4. 6 Binarisation Most minutiae extraction algorithms operate on fundamentally binary program images where there are only two levels of interest the black pixels represent ridges, and the white pixels represent valleys. Binarisation 1 converts a greylevel image into a binary image. This helps in improving the contrast between the ridges and valleys in a ? ngerprint image, and consequently facilitates the extraction of minutiae.One very useful property of the Gabor ? lter is that it contains a DC component of zero, which indicates that the resulting ? ltered image has a zero mean pixel value. Hence, binarisation of the image can be done by using a global threshold of zero. Binarisation involves examining the grey-level value of every pixel in the enhanced image, and, if the grey-level value is greater than the prede? ned global threshold, then the pixel value is set to value one else, it is set to zero. The outcome of binarisation is a binary image which contains two levels of information, the background valleys and the foreground ridges. . 7 Thinning Thinning is a morphological operation which is used to remove selected foreground pixels from the binary images. A standard thin algorithm from 1 is used, which performs this operation using two subiterations. The algorithm can be accessed by a software MATLAB via the thin operation of the bwmorph function. Each subiteration starts by examining the neighborhood of every pixel in the binary image, and on the basis of a particular set of pixel-deletion criteria, it decides whether the pixel can be outback(a) or not. These subiterations goes on until no more pixels can be outsi de.Figure 4. 2 (a)Original Image, (b)Enhanced Image, (c)Binarised Image, (d)Thinned Image Chapter 5 Feature Extraction After improving quality of the ? ngerprint image we extract features from binarised and thinned images. We extract reference point, minutiae and key(used for one to many matching). 5. 1 Finding the Reference Point Reference point is very important feature in advanced matching algorithms because it provides the location of origin for marking minutiae. We ? nd the reference point using the algorithm as in 2. Then we ? nd the relative position of minutiae and estimate the orientation ? ld of the reference point or the singular point. The technique is to extract affectionateness and delta points using Poincare Index. The value of Poincare index is 180o , ? 180o and 0o for a core, a delta and an ordinary point respectively. Complex ? lters are used to produce blur at di? erent resolutions. Singular point (SP) or reference point is the point of maximum ? lter response of these ? lters applied on image. Complex ? lters , exp(im? ) , of order m (= 1 and -1) are used to produce ? lter response. Four level resolutions are used herelevel 0, level 1, level 2, level 3.Level 3 is lowest resolution and level 0 is highest resolution. Only ? lters of ? rst order are used h = (x + iy)m g(x, y) where g(x,y) is a gaussian de? ned as g(x, y) = exp? ((x2 + y 2 )/2? 2 ) and m = 1, ? 1. Filters are applied to the complex valued orientation tensor ? eld image z(x, y) = (fx + ify )2 and not directly to the image. Here f x is the derivative of the original image in the x-direction and f y is the derivative in the y-direction. To ? nd the position of a possible 35 36 CHAPTER 5. FEATURE EXTRACTION Figure 5. 1 Row 1 ? lter response c1k , k = 3, 2, and 1. Row 2 ? ter response c2k , k = 3, 2, and 1. SP in a ? ngerprint the maximum ? lter response is extracted in image c13 and in c23 (i. e. ?lter response at m = 1 and level 3 (c13 ) and at m = ? 1 and level 3 (c23 )). The s earch is done in a window computed in the previous higher level (low resolution). The ? lter response at lower level (high resolution) is used for ? nding response at higher level (low resolution). At a certain resolution (level k), if cnk (xj , yj ) is higher than a threshold an SP is found and its position (xj , yj ) and the complex ? lter response cnk (xj , yj ) are noted. 5. 2 5. 2. 1Minutiae Extraction and Post-Processing Minutiae Extraction The most commonly employed method of minutiae extraction is the Crossing Number (CN) imagination 1 . This method involves the use of the skeleton image where the ridge ? ow pattern is eight-connected. The minutiae are extracted by scanning the local neighborhood of each ridge pixel in the image using a 3 x 3 window. The CN value is then computed, which is de? ned as half the sum of the di? erences between pairs of adjacent pixels in the eight-neighborhood. Using the properties of the CN as shown in ? gure 5, the ridge pixel can then be cla ssi? d as a ridge ending, bifurcation or non-minutiae point. For example, a ridge pixel with a CN of one corresponds to a ridge ending, and a CN of three corresponds to a bifurcation. 5. 2. MINUTIAE EXTRACTION AND POST-PROCESSING Table 5. 1 Properties of Crossing Number CN Property 0 isolated Point 1 Ridge Ending Point 2 Continuing Ridge Point 3 Bifurcation Point 4 Crossing Point 37 Figure 5. 2 Examples of (a)ridge-ending (CN=1) and (b)bifurcation pixel (CN=3) 5. 2. 2 Post-Processing dour minutiae may be introduced into the image due to factors such as noisy images, and image artefacts created by the thinning process.Hence, after the minutiae are extracted, it is necessary to employ a post-processing 1 stage in order to validate the minutiae. Figure 5. 3 illustrates some examples of false minutiae structures, which include the spur, hole, trilateral and spike structures . It can be seen that the spur structure generates false ridge endings, where as both the hole and triangle str uctures generate false bifurcations. The spike structure creates a false bifurcation and a false ridge ending point. Figure 5. 3 Examples of typical false minutiae structures (c)Triangle, (d)Spike (a)Spur, (b)Hole, 38 CHAPTER 5.FEATURE EXTRACTION 5. 2. 3 Removing Boundary Minutiae For removing boundary minutiae, we used pixel- niggardness approach. Any point on the boundary will have less white pixel absorption in a window centered at it, as compared to inner minutiae. We calculated the limit, which indicated that pixel density less than that means it is a boundary minutiae. We calculated it according to following formula limit = ( w w ? (ridgedensity)) ? Wf req 2 (5. 1) where w is the window size, Wf req is the window size used to compute ridge density. Figure 5. 4 Skeleton of window centered at boundary minutiaeFigure 5. 5 Matrix Representation of boundary minutiae Now, in thinned image, we sum all the pixels in the window of size w centered at the boundary minutiae. If sum is l ess than limit, the minutiae is considered as boundary minutiae and is discarded. 5. 3. EXTRACTION OF THE KEY 39 5. 3 5. 3. 1 Extraction of the key What is key? Key is used as a hashing tool in this project. Key is small set of few minutiae surrounding(prenominal) to reference point. We match minutiae sets, if the keys of ideal and query ? ngerprints matches. Keys are stored along with minutiae sets in the database.Advantage of using key is that, we do not perform full matching every time for non-matching minutiae sets, as it would be time consuming. For large databases, if we go on matching full minutiae set for every enrolled ? ngerprint, it would waste time unnecessarily. Two types of keys are proposed simple and complex. Simple key has been used in this project. Figure 5. 6 Key Representation Simple Key This type of key has been used in this project. Minutiae which constitute this key are ten minutiae closest to the reference point or centroid of all minutiae, in sorted 40 CH APTER 5. FEATURE EXTRACTION order. Five ? lds are stored for each key value i. e. (x, y, ? , t, r). (x, y) is the location of minutiae, ? is the value of orientation of ridge related to minutia with respect to orientation of reference point, t is type of minutiae, and r is distance of minutiae from origin. Due to inaccuracy and imperfection of reference point detection algorithm, we used centroid of all minutiae for construction of key. Complex Key The complex key stores more information and is structurally more complex. It stores vector of minutiae in which next minutiae is closest to previous minutiae, starting with reference point or centroid of all minutiae.It stores x, y, ? , t, r, d, ? . Here x,y,t,r,? are same, d is distance from previous minutiae entry and ? is di? erence in ridge orientation from previous minutiae. Data minutiaelist = Minutiae Set, refx = x-cordinate of centroid, refy = y-cordinate of centroid Result Key d(10)=null for j = 1 to 10 do for i = 1 to rows(minu tiaelist) do d(i) Chapter 6 Partitioning of Database Before we partition the database, we perform gender estimation and classi? cation. 6. 1 Gender Estimation In 3, study on 100 males and 100 females revealed that signi? cant waken di? erences occur in the ? ngerprint ridge density.Henceforth, gender of the candidate can be estimated on the basis of given ? ngerprint data. Henceforth, gender of the candidate can be estimated on the basis of given ? ngerprint data. Based on this estimation, searching for a record in the database can be made faster. Method for ? nding mean ridge density and estimated gender The highest and lowest values for male and female ridge densities will be searched. If ridge density of query ? ngerprint is less than the lowest ridge density value of females, the query ? ngerprint is obviously of a male. Similarly, if it is higher than highest ridge density value of males, the query ? gerprint is of a female. So the searching will be carried out in male or fema le domains. If the value is between these values, we search on the basis of whether the mean of these values is less than the density of query image or higher. 41 42 CHAPTER 6. PARTITIONING OF DATABASE Figure 6. 1 Gender Estimation 6. 1. GENDER ESTIMATION Data Size of Database = N Ridge Density of query ? ngerprint = s Result Estimated Gender i. e. male or female maleupperlimit=0 femalelowerlimit=20 mean=0 for image femalelowerlimit then femalelowerlimit 43 if s maleupperlimit then estimatedgender 44 CHAPTER 6.PARTITIONING OF DATABASE 6. 2 Classi? cation of Fingerprint We divide ? ngerprint into ? ve classes arch or tented arch, left loop, right loop, whorl and unclassi? ed. The algorithm for classi? cation 4 is used in this project. They used a ridge classi? cation algorithm that involves three categories of ridge structuresnon go on ridges, type I recurring ridges and type II recurring ridges. N1 and N2 represent number of type I recurring ridges and type II recurring ridges r espectively. Nc and Nd are number of core and delta in the ? ngerprint. To ? nd core and delta, separate 135o blocks from orientation image. 35o blocks are shown in following ? gures. Figure 6. 2 135o blocks of a ? ngerprint Based on number of such blocks and their relative positions, the core and delta are found using Poincare index method. After these, classi? cation is done as following 1. If (N2 0) and (Nc = 2) and (Nd = 2), then a whorl is identi? ed. 2. If (N1 = 0) and (N2 = 0) and (Nc = 0) and (Nd = 0), then an arch is identi? ed. 3. If (N1 0) and (N2 = 0) and (Nc = 1) and (Nd = 1), then classify the input using the core and delta assessment algorithm4. 4. If (N2 T2) and (Nc 0), then a whorl is identi? ed. 5.If (N1 T1) and (N2 = 0) and (Nc = 1) then classify the input using the core and delta assessment algorithm4. 6. If (Nc = 2), then a whorl is identi? ed. 7. If (Nc = 1) and (Nd = 1), then classify the input using the core and delta assessment algorithm4. 8. If (N1 0) and (Nc = 1), then classify the input using the core and delta assessment algorithm. 6. 3. PARTITIONING 9. If (Nc = 0) and (Nd = 0), then an arch is identi? ed. 10. If none of the above conditions is satis? ed, then reject the ? ngerprint. 45 Figure 6. 3 Fingerprint Classes (a)Left Loop, (b)Right Loop, (c)Whorl, (d1)Arch, (d2)Tented Arch . 3 Partitioning After we estimate gender and ? nd the class of ? ngerprint, we know which ? ngerprints to be searched in the database. We somewhat divide database into one-tenth using the above parameters. This would roughly reduce identi? cation time to one-tenth. 46 CHAPTER 6. PARTITIONING OF DATABASE Figure 6. 4 Partitioning Database Chapter 7 Matching Matching means ? nding most appropriate similar ? ngerprint to query ? ngerprint. Fingerprints are matched by matching set of minutiae extracted. Minutiae sets never match completely, so we compute match establish of matching. If match score satis? s accuracy needs, we call it successful matchi ng. We used a new key based one to many matching mean for large databases. 7. 1 Alignment Before we go for matching, minutiae set need to be aligned(registered) with each other. For alignment problems, we used hough transform based registration technique similar to one used by Ratha et al5. Minutiae alignment is done in two steps minutiae registration and pairing. Minutiae registration involves aligning minutiae using parameters ? x, ? y, ? which range within speci? ed limits. (? x, ? y) are translational parameters and ? is rotational parameter.Using these parameters, minutiae sets are rotated and translated within parameters limits. Then we ? nd pairing scores of each transformation and transformation giving maximum score is registered as alignment transformation. Using this transformation ? x, ? y, ? , we align query minutiae set with the database minutiae set. Algorithm is same as in 5 but we have excluded factor ? s i. e. the scaling parameter because it does not a? ect muc h the alignment process. ? lies from -20 degrees to 20 degrees in steps of 1 or 2 generalized as ? 1 , ? 2 , ? 3 ? k where k is number of rotations applied.For every query minutiae i we check if ? k + ? i = ? j where ? i and ? j are orientation 47 48 CHAPTER 7. twinned parameters of ith minutia of query minutiae set and j th minutia of database minutiae set. If condition is satis? ed, A(i,j,k) is ? agged as 1 else 0. For all these ? agged values, (? x, ? y) is calculated using following formula ? (? x , ? y ) = qj ? ? cos? sin? ? ? ? pi , (7. 1) ?sin? cos? where qj and pi are the coordinates of j th minutiae of database minutiae set and ith minutiae of query minutiae set respectively. Using these ? x, ?y, ? k values, whole query minutiae set is aligned.This aligned minutiae set is used to compute pairing score. Two minutiae are said to be paired only when they lie in same bounding box and have same orientation. Pairing score is (number of paired minutiae)/(total number of minut iae). The i,j,k values which have highest pairing score are ? nally used to align minutiae set. Co-ordinates of aligned minutiae are found using the formula ? qj = ? cos? sin? ? ? ? pi + (? x , ? y ), (7. 2) ?sin? cos? After alignment, minutiae are stored in sorted order of their distance from their centroid or core. 7. 2 Existing Matching TechniquesMost popular matching technique of today is the simple minded n2 matching where n is number of minutiae. In this matching each minutiae of query ? ngerprint is matched with n minutiae of sample ? ngerprint giving total number of n2 comparisons. This matching is very orthodox and gives headache when identi? cation is done on large databases. 7. 3 One to Many matching Few algorithms are proposed by many researchers around the world which are better than normal n2 matching. But all of them are one to one veri? cation or one to one identi? cation matching types. We developed a one to many matching technique which uses key as the hashing tool .Initially, we do not match minutiae sets instead we per- 7. 3. ONE TO MANY MATCHING 49 form key matching with many keys of database. Those database ? ngerprints whose keys match with key of query ? ngerprint, are allowed for full minutiae matching. Key matching and full matching are performed using k*n matching algorithm discussed in later section. Following section gives method for one to many matching. Data Query Fingerprint Result Matching Results Acquire Fingerprint, Perform Enhancement, Find Fingerprint Class, Extract Minutiae, Remove Spurious and Boundary Minutiae, Extract Key,Estimate Gender M . 3. 1 Method of One to Many Matching The matching algorithm will be involving matching the key of the query ? ngerprint with the many(M) keys of the database. Those which matches ,their full matching will be processed, else the query key will be matched with next M keys and so on. 50 Data Gender, Class, i Result Matching Results egender CHAPTER 7. MATCHING if keymatchstatus = success then eminutiae 7. 4 Performing key match and full matching Both key matching and full matching are performed using our k*n matching technique. Here k is a constant(recommended value is 15) chosen by us.In this method, we match ith minutiae of query set with k unmatched minutiae of sample set. Both the query sets and sample sets must be in sorted order of distance from reference point or centroid. ith minutia of query minutiae list is matched with top k unmatched minutiae of database minutiae set. This type of matching reduces matching time of n2 to k*n. If minutiae are 80 in number and we chose k to be 15, the total number of comparisons will reduce from 80*80=6400 to 80*15=1200. And this means our matching will be k/n times faster than n2 matching. 7. 5. TIME COMPLEXITY OF THIS MATCHING technique 51 Figure 7. One to Many Matching 7. 5 Time Complexity of this matching technique Let s = size of the key, n = number of minutiae, N = number of ? ngerprints matched till successful iden ti? cation, k = constant (see previous section). There would be N-1 unsuccessful key matches, one successful key match, one successful full match. Time for N-1 unsuccessful key matches is (N-1)*s*k (in worst case), for successful full match is s*k and for full match is n*k. Total time is (N-1)*s*k+n*k+s*k = k(s*N+n). Here s=10 and we have reduce database to be searched to 1/10th ,so N matching technique, it would have been O(Nn2 ).For large databases, our matching technique is best to use. Averaging for every ? ngerprint, we have O(1+n/N) in this identi? cation process which comes to O(1) when N n. So we can say that our identi? cation system has constant average matching time when database size is millions. Chapter 8 Experimental Analysis 8. 1 Implementation Environment We tested our algorithm on several databases like FVC2004, FVC2000 and Veri? nger databases. We used a computer with 2GB RAM and 1. 83 GHz Intel Core2Duo processor and softwares like Matlab10 and MSAccess10. 8. 2 8. 2. 1 Fingerprint Enhancement Segmentation and NormalizationSegmentation was performed and it generated a mask matrix which has values as 1 for ridges and 0 for background . Normalization was done with mean = 0 and variance = 1 (? g 8. 1). Figure 8. 1 Normalized Image 52 8. 2. FINGERPRINT ENHANCEMENT 53 8. 2. 2 Orientation Estimation In orientation estimation, we used block size = 3*3. Orientations are shown in ? gure 8. 2. Figure 8. 2 Orientation Image 8. 2. 3 Ridge Frequency Estimation Ridge density and mean ridge density were calculated. Darker blocks indicated low ridge density and vice-versa. Ridge frequencies are shown in ? gure 8. 3. Figure 8. 3 Ridge Frequency Image 8. 2. 4Gabor Filters Gabor ? lters were employed to enhance quality of image. Orientation estimation and ridge frequency images are requirements for implementing gabor ? lters. ?x and ? y are taken 0. 5 in Raymond Thai, but we used ? x = 0. 7 and ? y = 0. 7. Based on these values , we got results which were sat is? able and are shown in ? gure 8. 4. 54 CHAPTER 8. EXPERIMENTAL ANALYSIS Figure 8. 4 Left-Original Image, Right-Enhanced Image 8. 2. 5 Binarisation and Thinning After the ? ngerprint image is enhanced, it is then converted to binary form, and submitted to the thinning algorithm which reduces the ridge thickness to one pixel wide.Results of binarisation are shown in ? gure 8. 5 and of thinning are shown in ? gure 8. 6. Figure 8. 5 Binarised Image 8. 3. FEATURE EXTRACTION 55 Figure 8. 6 Thinned Image 8. 3 8. 3. 1 Feature Extraction Minutiae Extraction and Post Processing Minutiae Extraction Using the crossing number method, we extracted minutiae. For this we used skeleton image or the thinned image. Due to low quality of ? ngerprint, a lot of false and boundary minutiae were found. So we moved forward for post-processing step. Results are shown in ? gure 8. 7 and 8. 8. Figure 8. 7 All Extracted Minutiae 56 CHAPTER 8. EXPERIMENTAL ANALYSISFigure 8. 8 Composite Image with spurious and boundary minutiae After Removing Spurious and Boundary Minutiae False minutiae were removed using method described in earlier section. For removing boundary minutiae, we employed our algorithm which worked ? ne and minutiae extraction results are shown in table 8. 2. Results are shown in ? gure 8. 9 and 8. 10. Figure 8. 9 Minutiae Image after post-processing As we can see from table 8. 2 that removing boundary minutiae considerably reduced the number of false minutiae from minutiae extraction results. 8. 4. GENDER ESTIMATION AND CLASSIFICATION 57 Figure 8. 0 Composite Image after post-processing Table 8. 1 Average Number of Minutiae before and after post-processing DB After After Removing After Removing Used Extraction Spurious Ones Boundary Minutiae FVC2004DB4 218 186 93 FVC2004DB3 222 196 55 8. 3. 2 Reference Point Detection For reference point extraction we used complex ? lters as described earlier. For a database size of 300, reference point was found with success rate of 67. 6 6 percent. 8. 4 8. 4. 1 Gender Estimation and Classi? cation Gender Estimation Average ridge density was calculated along with minimum and maximum ridge densities shown in table 8. . Mean ridge density was used to divide the database into two parts. This reduced database size to be searched by half. Based on the information available about the gender of enrolled student, we can apply our gender estimation algorithm which will further increase the speed of identi? cation. 8. 4. 2 Classi? cation Fingerprint classi? cation was performed on both original and enhanced images. Results were more accurate on the enhanced image. We used same algorithm as in sec 6. 2 to classify the ? ngerprint into ? ve classes arch, left loop, right loop, whorl and 58 CHAPTER 8.EXPERIMENTAL ANALYSIS Figure 8. 11 Plotted Minutiae with Reference Point(Black Spot) Table 8. 2 Ridge Density Calculation Results Window borderline Maximum Mean Total Average Size Ridge Ridge Ridge Time Time interpreted Density Den sity Density Taken Taken 36 6. 25 9. 50 7. 87 193. 76 sec 1. 46 sec unclassi? ed. This classi? cation was used to divide the database into ? ve parts which would reduce the database to be searched to one-? fth and lastly making this identi? cation process ? ve times faster. Results of classi? cation are shown in table 8. 4, 8. 5 and 8. 6. 8. 5 EnrollingAt the time of enrolling personal details like name, semester, gender, age, roll number etc. were asked to input by the user and following features of ? ngerprint were saved in the database (1)Minutiae Set (2)Key (3)Ridge Density (4)Class Total and average time taken for enrolling ? ngerprints in database is shown in table 8. 6. MATCHING Table 8. 3 Classi? cation Results on Original Image Class No. of (1-5) Images 1 2 2 2 3 3 4 4 5 121 Table 8. 4 Classi? cation Results on Enhanced Image Class No. of (1-5) Images 1 8 2 3 3 3 4 6 5 112 59 8. 7. All the personal details were stored in the MS Access database and were modi? d by running sq l queries inside matlab. Fingerprint features were stored in txt format inside a separate folder. When txt ? le were used, the process of enrolling was faster as compared to storing the values in MS Access DB. It was due to the operating expense of connections, running sql queries for MS Access DB. 8. 6 Matching Fingerprint matching is required by both veri? cation and identi? cation processes. 8. 6. 1 Fingerprint Veri? cation Results Fingerprint veri? cation is the process of matching two ? ngerprints against each other to verify whether they belong to same person or not. When a ? gerprint matches with the ? ngerprint of same individual, we call it true accept or if it doesnt, we call it false reject. In the same way if the ? ngerprint of di? erent individuals match, we call it a false accept or if it rejects them, it is true reject. False Accept Rate (FAR) and False wane Rate (FRR) are the error rates which are used to express matching trustability. FAR is de? ned by the formula 60 CHAPTER 8. EXPERIMENTAL ANALYSIS Table 8. 5 Time taken for Classi? cation Image Average Total Taken Time(sec) Time(sec) Original 0. 5233 69. 07 Enhanced 0. 8891 117. 36 Table 8. Time taken for Enrolling No. of retentivity Average Total Images Type Time(sec) Time(hrs) 294 MS Access DB 24. 55 2. 046 60 MS Access DB 29. 37 0. 49 150 TXT ? les 15. 06 1. 255 F AR = FA ? 100, N (8. 1) FA = Number of False Accepts, N = Total number of veri? cations FRR is de? ned by the formula FR ? 100, N F RR = (8. 2) FR = Number of False Rejects. FAR and FRR calculated over six templates of Veri? nger DB are shown in table 8. 8. This process took approximately 7 hours. 8. 6. 2 Identi? cation Results and Comparison with Other Matching techniques Fingerprint identi? cation is the process of identifying a query ? gerprint from a set of enrolled ? ngerprints. Identi? cation is usually a slower process because we have to search over a large database. Currently we match minutiae set of query ? ngerpri nt with the minutiae sets of enrolled ? ngerprints. In this project, we store key in the database at the time of enrolling. This key as explained in sec 5. 3 helps in 8. 6. MATCHING Table 8. 7 Error Rates FAR FRR 4. 56 12. 5 14. 72 4. 02 61 Figure 8. 12 Graph Time taken for Identi? cation vs Size of Database(key based one to many identi? cation) reducing matching time over non-matching ? ngerprints. For non-matching enrolled ? gerprints, we dont perform full matching, instead a key matching. Among one or many keys which matched in one iteration of one to many matching, we allow full minutiae set matching. Then if any full matching succeeds, we perform post matching steps. This identi? cation scheme has lesser time complexity as compared to conventional n2 one to one identi? cation. Identi? cation results are shown in table 8. 9. The graph of time versus N is shown in ? gure 8. 13. Here N is the index of ? ngerprint to be identi? ed from a set of enrolled ? ngerprints. Size of databa se of enrolled ? ngerprints was 150. So N can vary from

The Natural Cycle of Humanity and the Decay of Modern Society in The Wasteland

There is no romance, no passion, only a mundane circular sequence of events, crowds of people, walking round in a ring (56). In The Waste polish, by T. S. Eliot, the purchase order of the twentieth century is described as detached, dreary and mo nononous. It is a collection of dysfunctional relationships and tedious tasks, saturated with an misgiving about death. There is a parallel between the atrophy of company and the land destroyed during the Second piece War. To escape a routine and so-so(p) existence, humans strive for the unattainable, to overcome the limits of humanity.However any de disassembleure from the natural cycle of the human world leads to the emergence of the thriftlessness. Although death haunts the speakers in the poem, it is liberation in comparison to the horror of the dissipation. There is persistent angst and fear of death in the poem, yet death is everywhere. The many speakers in the poem wish for immortality and to overcome the confines of humanity. In The Burial of the Dead the woman, anxious about her fate, goes to see the fortune-teller, Madame Sosostris, who pulls out the Hanged Man tarot card and warns her to fear death by water (55).The fortune-tellers voice communication reoccur later in Death by Water, a description of the grotesque death of Phlebas the Phoenician. His death, symbolized by the whirlpool, confirms that there is no regeneration there is no return from the whirlpool. The realization of the fortune implies that fate cannot be defeated. In What the Thunder Said Eliot again states that there is no escape from death He who was brio in now dead/ We who are living are now dying (328-329).In The Burial of the Dead the speaker desires to abandon memories, he describes chute as cruel it causes sorrowful memories to resurface, while winter kept us warm/ covering Earth in forgetful snow (5-6). What he does not realize is that human existence is a collection of fragments that distinct memories in an ongoing cycle , illustrated in the first stanza of The Burial of the Dead. Abandonment of memories leads to a futile existence. The wasteland first appears in the second stanza of The Burial of the Dead contrasting the first stanza, which is full of life and memories.The narrator is separated from the natural course of existence and is addressing a person of the human world, Son of man ( ) for you only know a heap of broken images (20-23). The listener is part of the human cycle, he is still part of time Your shadow at morning striding behind you/ Or your shadow rising to meet you (28-29). He does not understand the true fear that comes one time time ceases to exist the way the speaker does I will show you fear in a handful of dust (30). The speaker has disconnected from society and drifted into the wasteland, suggested by Eliots diction stony rubbish, dead tree, ironical stone, dust.Only there has he discovered the true gist of fear an unearthly abyss. The wasteland is a situation or a place more terrifying than human imagination can conceive. It is complete emptiness, innocuous of the structures of person, place and time. Without time memories become meaningless repetitions and cease to exist. The epigram at the beginning of the poem introduces the immortal character Sibyl. Sybil is detached from the rest of the world by her cursed immortality and lives withering away and shriveled up, longing for death, the only escape from her suffering.The other immortal character in the poem, Tiresias, is blind, throbbing between two lives (line 218), as well as alienated from the human world, not only by his immortality but also because he is a hermaphrodite. Sybil and Tiresiass separation from the sequence of life compel them to lead a blue existence. The voices of these immortal characters portray how only once immortality is experienced can death become a salvation, a place of peace. The ripe relationships that Eliot portrays are impeccant of love, companionship and desir e.The theme when love fails, a wasteland develops is recurring throughout the poem. The author constantly alludes to the legend of the Fisher King. In the legend, The Fisher King was disadvantage and became impotent and ill, disabling him to care for his kingdom. He was left alone to lead a meaningless life, fishing. Without his love the land deteriorated, lost its fertility and perished into the wasteland. Similarly, in the modern society, alienation from the natural world and a depletion of love leads to decay.The woman in A Game of cheater attempts to speak to her significant other, distressed about their relationship. She pleads with him to gravel with her, to speak to her and to share his thoughts with her (111-113). He is detached, remaining silent and thinking only of death. The man has separated from humanity while the woman remains part of the circular existence. The couple remains together yet their relationship has become a wasteland there is nothing between them. In A Game of Chess, Lil and Alberts relationship is presented though a conversation in a pub.Lil is revolting to Albert, he tells her that he cannot even bare to look at her (144). Lils body is disintegrating, a consequence of the pills, spring birthn to her by the pharmacist, that she took to induce an abortion. They caused her to drastically age and lose her teeth. Lils desire to not have children is portrayed as unnatural, What you get married for it you dont loss children? (164). Lils actions lead to her body becoming a wasteland. The encounter between the banker and the typist in The Fire Sermon again manifests the absence of love.Their meeting is solely sexual and indigent of any feelings. Even the sex holds no pleasure and is non-reproductive. The woman is indifferent to their relations and upon his departure thinks Well now thats over and Im glad its over (252), as if she had completed another chore. These series of affairs reflect the atmosphere of the society, the lack o f intimacy and the disconnection of human relations. The wasteland is a consequence of the failure to care, to love, to give birth and to partake in the cycle. T. S Eliot creates a parallel between the wretched land of the Fisher King and the slaughter, destruction and ruin created by World War II.The barren landscape left by World War II reflects the inner decay of humanity the same way the sterile land of the Fisher King is an outward projection of his inner sickness. The desolate landscape of the wasteland described in the beginning of the poem, returns along with the character of the Fisher King. Eliot describes the miserable condition of the wasteland, sterile, dry and unbearable.He creates a surreal image of a desert mountains of rock without water, endless plains, cracked earth (370), and bats with small fry faces in the violet light (380). This place transforms into the barren kingdom of the Fisher King, suggested by the empty chapel, which is an allusion to the Chapel Peri lous. In the legend of the Holy Grail, Parsifal put together the Holy Grail in the Chapel Perilous and life returned to the land. However, in the empty chapel in the poem there are only dry bones, signifying that vitality will not return to the land like it does in the legend.Instead society continues to decay illustrated in the line London brace is falling down (427). In reality there is no Holy Grail, there is no change I sat upon the shore/ Fishing with the dried-out plain behind me (424-425). The banal, circular sequence of human life continues. Eliot explores the themes of life, death, immortality and alienation throughout The Wasteland. These themes are examined in various historical contexts, from ancient myths to the modern society and tied together by the immortal characters, Sibyl and Tiresias. Disconnected by the varying historical context and the many narrators, T. S. Eliots style of writing in The Wasteland mirrors the disintegrated moments that give meaning to human life.Human life is cyclical, routine and mundane with memories as the only specks of color on an otherwise gray canvas. Death is not an remainder it is only part of the cycle. Immortality, the desire to forget and deprivation of emotion and of love are unnatural and create a partition from the human world where the wasteland appears. Modern Society consists of failed relationships and hollow humans existing in the Unreal City. Its loss of fertility and love results in the emergence of a wasteland.

Premartial Sex

This paper go away include my research on pre marital wake up. For many years, premarital sex has been seen as a type of deviant manner exclusively like many otherwise concepts, deviant behavior can be define in many ways. This research will include a clear definition of deviant behavior and its relationship with premarital sex. Deviant Behavior Defined According to the Sociology Index, deflection is nonconformity to social norms. However, often deviance is simply conformity to the norms or standards of a subgroup or subculture rather than those of the dominant culture.Deviance is not inherent in any behavior or attitude but rather is a result of human interaction in particular normative situations. Deviant behavior usually evokes formal and informal punishment, restrictions, or other controls of society. These formal and informal controls constrain most people to conform to social norms. Despite the social sanctioning and controlling, however, we sometimes observe deviant beh avior close to us, with premarital sex being iodine. (Sociology Index).What is Premarital Sex? Premarital sex is often referred to as fornication, meaning voluntary sexual intercourse between 2 unmarried persons or two persons not married to each other according the Webster dictionary. Fornication can also be found in the watchword and is considered a major sin. Premarital Sex in America Almost all Americans have sex before marrying, according to premarital sex research that shows such behavior is the norm in the U. S. and has been for the past 50 years.The new study shows that by age 20, 75% of Americans have had premarital sex. That number rises to 95% by age 44. Even among those who abstained from sex until 20 or beyond, 81% have had premarital sex by 44, the survey shows. Researchers say the findings question the feasibleness of federally funded abstinence-only education programs. Premarital sex is normal behavior for the vast majority of Americans, and has been for decades, says researcher Lawrence Finer, director of domestic research at the Guttmacher Institute, in a news release. The data clearly show that the majority of older teens and adults have already had sex before marriage, which calls into question the federal governments funding of abstinence-only-until-marriage programs for 1229-year-olds. It would be more effective to provide young people with the skills and information they need to be safe once they become sexually quick which nearly everyone last will, says Finer.In the study, published in Public Health Reports, researchers analyzed data from four cycles of the National Survey of Family Growth from 1982 to 2002, which included information on sexual and marital behaviors. The results showed that the vast majority of Americans have sex before marrying. For example, the 2002 survey showed By age 20, 77% of men and women had had sex, including 75% who had had premarital sex. By age 44, 95% of men and women had had premarital sex 97% of t hose who had ever had sex had had premarital sex.Among those who had abstained from sex until at least age 20, 81% had had premarital sex by age 44. Despite public opinion that premarital sex is much more common now than in the past, researchers say the number of Americans having premarital sex has not changed much since the 1940s. Among women who turned 15 between 1964 and 1993, 91% had had premarital sex before age 30, compared with 82% of women who turned 15 between 1954 and 1963. In addition, nearly nine out of 10 women who turned 15 between 1954 and 1963 had had unmarried sex by age 44.Researchers say that though the likelihood that Americans will have sex before marriage hasnt changed significantly since the 1950s, people are now waiting longer to get married. So they are sexually active and unmarried for longer than in the past (WebMD). In Janet Smiths article about Premarital Sex, she states The evidence is overwhelming that children brocaded in households headed by a singl e parent are much more prone to sexual abuse, drug abuse, crime, and divorce, for instance.Their health is poorer their academic achievement is poorer their economic well-being is slight than that of children who are raised in two-parent households. In every way, children raised in single parent households seem to have a few strikes against them as they make their way through life. (I do not want to suggest, of course, that all children raised in single parenthood households are doomed. I simply want to give out that Catholic Church teaching, the teaching of most religions, sociological research, and perhaps common sense are at one in recognizing that children fare better when raised in a household with two parents. The number of single-parenthood households has risen dramatically, due, of course, largely to unwed pregnancy and divorce ( Catholic Education Resource Center). Also tell in this article by Janet Smith, The dimensions of the problem of unwed pregnancy are very seriou s, indeed. In the early nineteen sixties, some 3% of whiteness babies were born(p) out of wedlock, some 22% of black babies and as a whole, 6% of the babies born in the United States were born to unwed parents.Now some 22% of white babies, 68% of black babies and as an aggregate in the United States some 31% of babies are born to unwed parents. One out of four to one out of three pregnancies in the United States are ended through abortion, the vast majority performed on unmarried women. Nearly every one of these births and abortions institute a failed relationship, a relationship that was not committed to the caring for any children that may be conceived through the relationship ( Catholic Education Resource Center).