Master Thesis - Department of Computer Science

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Table of Contents List of Tables xi List of Figures xiv Abstract xviii 1 Introduction 1 1.1 Biometric System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.1 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.2 Motivation behind Face-based Biometric System . . . . . . . . 3 1.2 Multimodal Biometric System . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Brief Description of the Work Done . . . . . . . . . . . . . . . . . . . 5 1.4 Overview of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Literature Review 9 2.1 Recent Approaches to Face Recognition . . . . . . . . . . . . . . . . . 9 2.1.1 Template based Methods . . . . . . . . . . . . . . . . . . . . . 11 2.1.2 Geometry Feature based Methods . . . . . . . . . . . . . . . . 23 2.2 Recent Approaches to Fingerprint Recognition . . . . . . . . . . . . . 24 2.2.1 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.2.2 Feature Representation and Feature Extraction . . . . . . . . 28 2.2.3 Fingerprint Matching . . . . . . . . . . . . . . . . . . . . . . . 31 2.3 Recent Approaches to Multiple Classifier Combination . . . . . . . . 32 2.3.1 Architecture or Operational Mode . . . . . . . . . . . . . . . . 32 2.3.2 Fusion Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.3.3 Fusion Levels and Methods of Integration . . . . . . . . . . . 35
3 An Efficient Method of Face Recognition using Subject-Specific Sub- band Faces 44 3.1 Introduction and Motivation . . . . . . . . . . . . . . . . . . . . . . . 45 3.2 Subband Face Representation . . . . . . . . . . . . . . . . . . . . . . 48 3.2.1 Wavelet Decomposition . . . . . . . . . . . . . . . . . . . . . 48 3.2.2 Subband Face Reconstruction . . . . . . . . . . . . . . . . . . 49 3.3 Proposed Methods for Subject-Specific Subband Selection . . . . . . . 53 3.3.1 Criteria used for measuring Goatishness and Lambishness . . . 55 3.3.2 Subject-Specific Subband Selection Algorithm . . . . . . . . . 57 3.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.4.1 Databases Used . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.4.2 Performance Analysis on Three Standard Face Databases . . . 61 3.4.3 Comparison with Ekenel’s Multiresolution Face Recognition [36] 64 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4 Dual Space Face Recognition using Feature and Decision Fusion 70 4.1 Subspace Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.2 Obtaining Eigenmodels in Range Space and Null Space of Within-class Scatter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.3 Feature Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.3.1 Techniques for Merging Eigenmodels . . . . . . . . . . . . . . 77 4.3.2 Search Criterion and Techniques for Optimal Feature Selection 79 4.3.3 Algorithm for Feature Fusion . . . . . . . . . . . . . . . . . . 81 4.4 Decision Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.4.1 Existing Techniques for Decision Fusion . . . . . . . . . . . . 83 4.4.2 Proposed Technique for Decision Fusion . . . . . . . . . . . . 84 4.4.3 Algorithm for Decision Fusion . . . . . . . . . . . . . . . . . . 90 4.5 Experimental Results and Discussion . . . . . . . . . . . . . . . . . . 91 4.5.1 Effect of Number of Training Samples on the Performance of Null Space and Range Space . . . . . . . . . . . . . . . . . . . 92 viii
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: LDC Linear Discriminant Classifier
Page 11 and 12: A.6 Minutiae Matching . . . . . . .
Page 13 and 14: 4.1 Effect of increasing number of
Page 15 and 16: List of Figures 2.1 Summary of appr
Page 17 and 18: 3.7 Area difference between genuine
Page 19 and 20: Abstract Biometrics is a rapidly ev
Page 21 and 22: CHAPTER 1 Introduction The issues a
Page 23 and 24: 1.1.1 Applications Biometrics has b
Page 25 and 26: • Spoof Attacks: An impostor may
Page 27 and 28: • A new approach for multimodal b
Page 29 and 30: CHAPTER 2 Literature Review This re
Page 31 and 32: ange [23], infrared scanned [137] a
Page 33 and 34: u 2 x 2 u 1 x 1 (a) PCA basis (b) P
Page 35 and 36: PCA Projection Class 1 Class 2 LDA
Page 37 and 38: F DIFS DFFS F (a) (b) F 1 L Figure
Page 39 and 40: image A of m rows and n columns is
Page 41 and 42: faces of 40 subjects. • Others: A
Page 43 and 44: malizing the vector components, the
Page 45 and 46: Figure 2.5: A fingerprint image wit
Page 47 and 48: features (gradient coherence, inten
Page 49 and 50: Figure 2.7: Examples of minutiae; A
Page 51 and 52: according to the local orientation
Page 53 and 54: • Parallel Mode: This operational
Page 55 and 56: can address the problem of noisy se
Page 57 and 58: Prior to Matching Sensor Level Feat
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determined by logistic regression.
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Class-indifferent Methods • Decis
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3. The final DS soft vector is calc
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on three face databases and compare
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for a face. This was illustrated by
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Image Rows h(.) g(.) 2 2 Columns Fi
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in the accuracy of face recognition
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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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