Master Thesis - Department of Computer Science

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(noglasses) (centerlight) (happy) (glasses) (1) (2) (3) (1) (2) (3) (4) Figure 4.7: Training samples for three databases: First, second and third row shows the training set for Yale, ORL, PIE databases, respectively. Table 4.5: Performance of null space and range space on Yale, ORL and PIE Databases. Methods Null Space Range Space Yale 81.67 75.00 ORL 79.17 71.67 PIE 86.35 73.14 98
Table 4.6: Performance of Dual Space Face Recognition on Yale, ORL and PIE databases. PLDA and PNLDA are our proposed decision fusion techniques, using LDA and nonparametric LDA respectively. BS: Backward Selection; FS: Forward Selection. Feature Fusion Decision Fusion Covariance Sum Gramm-Schmidt Existing Techniques Proposed Methods Databases BS FS BS FS Sum Product PLDA PNLDA Yale 85.00 85.00 85.00 85.00 86.67 86.67 86.67 86.67 ORL 85.83 85.93 85.83 85.99 81.67 81.67 96.67 97.50 PIE 88.40 88.40 87.28 87.35 83.72 83.85 100.00 100.00 4.6 Conclusion In this chapter, a dual space based face recognition approach using feature and decision fusion from null space and range space of within-class scatter is proposed. The discriminative directions spread over the total face space. Null space and range space of with-class scatter constitutes the complete face space. So finding discriminative direction only in null space ignores the utilization of discriminative directions present in range space. We tried to merge the discriminative directions available in both spaces using covariance sum and Gramm-Schmidt Orthonormalization method. We also combine the complementary discriminatory information present in null space and range space by decision fusion. Along with sum and product rule for decision fusion we propose a new technique which learns both classifier’s (built on null space and range space) behavior to enhance class separability (using LDA and nonparametric LDA) at decision level using classifier’s response as training information. Experimen- tal results on three standard databases show that, feature and decision fusion indeed combines information from both spaces to improve classification performance. 99
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DEVELOPMENT OF EFFICIENT METHODS FO
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To My Parents, Sisters and Dearest
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Mirnalinee, Shreyasee, Lalit, Manis
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LDC Linear Discriminant Classifier
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3 An Efficient Method of Face Recog
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A.6 Minutiae Matching . . . . . . .
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4.1 Effect of increasing number of
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List of Figures 2.1 Summary of appr
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3.7 Area difference between genuine
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Abstract Biometrics is a rapidly ev
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CHAPTER 1 Introduction The issues a
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1.1.1 Applications Biometrics has b
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• Spoof Attacks: An impostor may
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• A new approach for multimodal b
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CHAPTER 2 Literature Review This re
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ange [23], infrared scanned [137] a
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u 2 x 2 u 1 x 1 (a) PCA basis (b) P
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PCA Projection Class 1 Class 2 LDA
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F DIFS DFFS F (a) (b) F 1 L Figure
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image A of m rows and n columns is
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faces of 40 subjects. • Others: A
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malizing the vector components, the
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Figure 2.5: A fingerprint image wit
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features (gradient coherence, inten
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Figure 2.7: Examples of minutiae; A
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according to the local orientation
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• Parallel Mode: This operational
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can address the problem of noisy se
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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
Page 67 and 68: for a face. This was illustrated by
Page 69 and 70: Image Rows h(.) g(.) 2 2 Columns Fi
Page 71 and 72: in the accuracy of face recognition
Page 73 and 74: (a) (a1) (a2) (a3) (b) (b1) (b2) (b
Page 75 and 76: Genuine Impostor Scores Scores 1 0
Page 77 and 78: Error d1 = (1 − TZeroF RR) d2 = |
Page 79 and 80: The algorithm for obtaining the sub
Page 81 and 82: each subject, 42 samples (flashes f
Page 83 and 84: Table 3.3: Peak Recognition Accurac
Page 85 and 86: Table 3.5: Peak Recognition Accurac
Page 87 and 88: Table 3.7: Performance of Ekenel’
Page 89 and 90: Table 3.9: Performance of Ekenel’
Page 91 and 92: to our proposed method. Section 4.2
Page 93 and 94: plored both of the spaces to captur
Page 95 and 96: project only the class means in ran
Page 97 and 98: ΩNull and ΩRange represents the
Page 99 and 100: Thus, calculation of the QR factori
Page 101 and 102: 4.3.3 Algorithm for Feature Fusion
Page 103 and 104: The architecture of our proposed me
Page 105 and 106: T R T S denoted by RVNull and RVNul
Page 107 and 108: x DNull DRange DP (x) DNull(x) DRan
Page 109 and 110: C 2 C 1 C 1 C 2 C X X 2 M X M X 2 1
Page 111 and 112: iii) Project all class means onto n
Page 113 and 114: Table 4.1: Effect of increasing num
Page 115 and 116: Table 4.3: Effect of increasing num
Page 117: (Original face) (Null face) (Range
Page 121 and 122: 5.1 Introduction In recent years, t
Page 123 and 124: This means for a classifier D: r i
Page 125 and 126: validation1, validation2 and test.
Page 127 and 128: 5.3.1 The Algorithm The steps of ou
Page 129 and 130: 5.4 Experimental Results In this ch
Page 131 and 132: sets. • We put as much as possibl
Page 133 and 134: argument is invalid in the context
Page 135 and 136: LDA and LDA produce the same accura
Page 137 and 138: space. If the training data uniform
Page 139 and 140: face for each subject, and compare
Page 141 and 142: also be introduced. But class speci
Page 143 and 144: These stages are discussed in the f
Page 145 and 146: (a) (b) Figure A.2: Input fingerpri
Page 147 and 148: (a) (b) Figure A.4: Orientation ima
Page 149 and 150: (i) For each block centered at (i,
Page 151 and 152: (a) (b) Figure A.7: Enhanced images
Page 153 and 154: (a) (b) Figure A.10: (a) and (b) sh
Page 155 and 156: (a) (b) Figure A.12: Binarized imag
Page 157 and 158: (a) (b) Figure A.14: Thinned images
Page 159 and 160: (a) (b) Figure A.16: Thinned finger
Page 161 and 162: Paired Minutiae Minutiae with unmat
Page 163 and 164: Bibliography [1] FVC 2002 website.
Page 165 and 166: [25] Li-Fen Chen, Hong-Yuan Mark Li
Page 167 and 168: [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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