Master Thesis - Department of Computer Science

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List of Tables 3.1 Sample distribution (per subject) in training, validation and testing sets for Yale, PIE and ORL databases. . . . . . . . . . . . . . . . . . 62 3.2 Peak Recognition Accuracy (PRA) and EER of original gray-level face image with PCA, LDA, 2D-PCA, 2D-LDA and DCV for Yale, PIE and ORL databases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.3 Peak Recognition Accuracy (PRA) and EER of subband face represen- tation integrated with PCA, LDA, 2D-PCA, 2D-LDA and DCV with subject-specific subbands obtained using four criteria on Yale database. 63 3.4 Peak Recognition Accuracy (PRA) and EER of subband face represen- tation integrated with PCA, LDA, 2D-PCA, 2D-LDA and DCV with subject-specific subbands obtained using four criteria on PIE database. 63 3.5 Peak Recognition Accuracy (PRA) and EER of subband face represen- tation integrated with PCA, LDA, 2D-PCA, 2D-LDA and DCV with subject-specific subbands obtained using four criteria on ORL database. 65 3.6 Best performing and successful subbands for Ekenel’s multiresolution face recognition [36] for Yale, PIE and ORL databases determined on testing set. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 3.7 Performance of Ekenel’s multiresolution face recognition [36] for Yale, PIE and ORL databases based on the successful subbands determined on testing set. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.8 Best performing and successful subbands for Ekenel’s multiresolution face recognition [36] for Yale, PIE and ORL databases determined on validation set. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.9 Performance of Ekenel’s multiresolution face recognition [36] for Yale, PIE and ORL databases based on the successful subbands determined on validation set. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.1 Effect of increasing number of training samples on the performance of null space and range space for Yale database. . . . . . . . . . . . . . . 93 4.2 Effect of increasing number of training samples on the performance of null space and range space for ORL database. . . . . . . . . . . . . . 94 4.3 Effect of increasing number of training samples on the performance of null space and range space for PIE database. . . . . . . . . . . . . . . 95 4.4 Sample distribution (per subject) in training, validation and testing sets for Yale, ORL and PIE databases. . . . . . . . . . . . . . . . . . 96 4.5 Performance of null space and range space on Yale, ORL and PIE Databases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 4.6 Performance of Dual Space Face Recognition on Yale, ORL and PIE databases. PLDA and PNLDA are our proposed decision fusion tech- niques, using LDA and nonparametric LDA respectively. BS: Back- ward Selection; FS: Forward Selection. . . . . . . . . . . . . . . . . . 99 5.1 Sample distribution (per subject) in train, validation and test sets for face databases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 5.2 Sample distribution (per subject) in train, validation and test sets for fingerprint databases. . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 5.3 Some test cases to simulate different type (performance-wise) of classifier combination. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 5.4 Base classifier’s performance (in Percentage Accuracy) for four face and fingerprint databases (two each). . . . . . . . . . . . . . . . . . . 112 5.5 Base classifier’s performance (in Percentage Accuracy) using LDA for the face and fingerprint databases (two each). . . . . . . . . . . . . . 112 5.6 Base classifier’s performance (in Percentage Accuracy) using nonparametric LDA for the face and fingerprint databases (two each). . . . . 113 5.7 Combined performance (in Percentage Accuracy) with PIE and F PA databases for different decision fusion strategies. . . . . . . . . . . . . 114 5.8 Combined performance (in Percentage Accuracy) with PIE and F PB databases for different decision fusion strategies. . . . . . . . . . . . . 114 xii
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
Page 11: A.6 Minutiae Matching . . . . . . .
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
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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
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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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