Master Thesis - Department of Computer Science

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1.1 Biometric System Biometrics is a term that encompasses “the application of modern statistical methods to the measurements of biological objects” [4]. Hence, biometric recognition refers to the use of distinctive physiological and behavioral characteristics (e.g. face, fingerprint, hand geometry, iris, gait, signature), called biometric identifiers or simply biometrics, for automatically recognizing a person. This has been used in several domains, such as person authorization examination in e-Banking and e-Commerce transactions or within the framework of access controls for security areas. Ideally the biometric characteristics used should satisfy the following properties: • Robustness: This means that the biometric should be sufficiently invariant (Permanence) over a period of time and thus maintain a low intra-class variability. • Distinctiveness: This indicates that biometric identifiers should differentiate (Uniqueness) any two persons and thus have large inter-class variability. • Availability: Ideally, a biometric identifier should be possessed by every person (Universality). • Accessibility: The characteristic should be easy to acquire (Collectability). A biometric system is essentially a pattern-recognition system. Such system involves three aspects: data acquisition and preprocessing, data representation, and decision making. It can thus compare a specific set of physiological or behavioral characteristics extracted from a person with a template/model acquired beforehand, and recognize the individual. The digital representation recorded in a database as a description of a physical trait is defined as a template and is obtained by feature extraction algorithms. Among different traits the motivations behind using face and fingerprint for person authentication are manifold. To explain the impetus behind the use of any biometric system, some important applications are described in the following section. 2
1.1.1 Applications Biometrics has been widely used in forensics applications such as criminal identifi- cation and prison security. It also has a strong potential to be widely adopted in civilian applications such as e-Banking, e-Commerce, and access control. Due to the huge increase in the use of electronic transactions, e-Banking and e-Commerce are becoming the most important emerging application of biometrics. Credit card and smart card security, ATM security, check cashing and fund transfers, online transactions and web access are some of the examples of the applications. The token-based authentication used in physical access control are gradually replaced by biometric systems. Similarly, knowledge-based authentication (e.g. password) for remote login and data access applications are substituted by biometrics. Other biometric applications include welfare disbursement, immigration checkpoints, national ID, voter and driver registration, and time and attendance. 1.1.2 Motivation behind Face-based Biometric System The face is one of the most acceptable biometrics, and it has also been the most common method of recognition that human use in their visual interactions. The problem with authentication systems based on fingerprint, voice, iris and the most recent gene structure (DNA fingerprint) has been the problem of data acquisition. For example, for fingerprint the concerned person should keep his/her finger in proper position and orientation and in case of speaker recognition the microphone should be kept in proper position and distance from the speaker. But, the method of acquiring face images is nonintrusive and thus face can be used as a biometric trait for covert (where user is unaware that he is being subjected) system. Face is a universal feature of human beings. Face recognition is important not only due to the capability of its lot of potential applications in research fields but also due to the capability of its solution which would help in solving other classification problems like object recognition. 3
Page 1 and 2: DEVELOPMENT OF EFFICIENT METHODS FO
Page 3 and 4: To My Parents, Sisters and Dearest
Page 5 and 6: Mirnalinee, Shreyasee, Lalit, Manis
Page 7 and 8: LDC Linear Discriminant Classifier
Page 9 and 10: 3 An Efficient Method of Face Recog
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Page 19 and 20: Abstract Biometrics is a rapidly ev
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(a) (a1) (a2) (a3) (b) (b1) (b2) (b
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Genuine Impostor Scores Scores 1 0
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Error d1 = (1 − TZeroF RR) d2 = |
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The algorithm for obtaining the sub
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each subject, 42 samples (flashes f
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Table 3.3: Peak Recognition Accurac
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Table 3.5: Peak Recognition Accurac
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Table 3.7: Performance of Ekenel’
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Table 3.9: Performance of Ekenel’
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to our proposed method. Section 4.2
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plored both of the spaces to captur
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project only the class means in ran
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ΩNull and ΩRange represents the
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Thus, calculation of the QR factori
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4.3.3 Algorithm for Feature Fusion
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The architecture of our proposed me
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T R T S denoted by RVNull and RVNul
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x DNull DRange DP (x) DNull(x) DRan
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C 2 C 1 C 1 C 2 C X X 2 M X M X 2 1
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iii) Project all class means onto n
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Table 4.1: Effect of increasing num
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Table 4.3: Effect of increasing num
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(Original face) (Null face) (Range
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Table 4.6: Performance of Dual Spac
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5.1 Introduction In recent years, t
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This means for a classifier D: r i
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validation1, validation2 and test.
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5.3.1 The Algorithm The steps of ou
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5.4 Experimental Results In this ch
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sets. • We put as much as possibl
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argument is invalid in the context
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LDA and LDA produce the same accura
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space. If the training data uniform
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face for each subject, and compare
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also be introduced. But class speci
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These stages are discussed in the f
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(a) (b) Figure A.2: Input fingerpri
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(a) (b) Figure A.4: Orientation ima
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(i) For each block centered at (i,
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(a) (b) Figure A.7: Enhanced images
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(a) (b) Figure A.10: (a) and (b) sh
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(a) (b) Figure A.12: Binarized imag
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(a) (b) Figure A.14: Thinned images
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(a) (b) Figure A.16: Thinned finger
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Paired Minutiae Minutiae with unmat
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Bibliography [1] FVC 2002 website.
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[25] Li-Fen Chen, Hong-Yuan Mark Li
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[49] Lin Hong, Yifei Wan, and Anil
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[71] L. Lam and C. Y. Suen. Applica
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IEEE Tran. on Pattern Analysis and
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of-the-Art Systems. IEEE Tran. on P
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and Human Communication, pages 374-
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Master Thesis - Department of Computer Science

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