Master Thesis - Department of Computer Science

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LDA and nonparametric LDA on them to enhance class separability at classifier response level and then combine them using sum rule. We refer to the set of class scores provided by a classifier on a sample, as response vector. Response vectors are used as features vectors at decision level and we then employ LDA and nonparametric LDA to enhance class separability at classifier output space. Response vectors on a validation set (disjoint from training and testing sets of a database) are used as training data at decision level. Then the response vector for a test case is recalculated in the newly formed eigenmodel (LDA or nonparametric LDA-based) to improve the combined classification accuracy. Decision fusion using both PLDA and PNLDA provide 100% on PIE database. • In chapter 5, we describe a new approach to combine decisions from face and fingerprint classifiers for multimodal biometry by exploiting the individual classifier space on the basis of availability of class specific information present in the classifier space. We train the face classifier using response vectors on a validation set for enhancing class separability using LDA and nonparametric LDA in the classifier output space and thereby improving performance of the face classifier. Fingerprint classifier does not often provide this information due to high sensitivity of available minutiae points, producing partial matches across subjects. The enhanced face and fingerprint classifiers are combined using sum rule. We also propose a generalized algorithm for Multiple Classifier Combination (MCC) based on our approach. Experimental results on two face and two fingerprint databases exhibit the superiority of the proposed method over other already existing fusion techniques. 6.2 Scope of Future Work Here we provide some of the open problems that can be attempted: 1. In chapter 3, we proposed four different criteria for selecting optimum subject- specific subband face. We believe a better criterion can indeed improve the performance of the technique. 2. In the same context, the introduction of subject-specific subspace method can 120
also be introduced. But class specific linear subspace method requires sufficient number of training samples per class which can be attained by generating multiple face samples (image rendering using 3D model of a face) from a single or a few face images. 3. The subband face introduced in this thesis is obtained by suppressing approx- imation and in some case, eliminating lower level details from dyadic decom- posed subbands. The search of optimal subband face can be extended by using a full tree decomposition with 2D-DWT and designing a more efficient algorithm and criterion to accomplish the search procedure. 4. In chapter 4, we combined null space and range space of within-class scatter. Different combination strategies at feature and decision level can be explored to improve the classification accuracy. 5. One can investigate the scope of combining subband face representation with dual space method to implement a superior face recognition system. 6. Effect of weighted modular PCA [67] on subband face representation can also form a nice extension of subband face based recognition system. 7. In chapter 5, face and fingerprint are combined using an efficient decision combination technique. Here, one can explore superior techniques for decision fusion of face and fingerprint classifiers. A robust multimodal biometrics can be de- veloped based on three or more cues (face, fingerprint, iris, voice, palm-print, etc.) using our proposed method of multiple classifier combination. 121
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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
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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’
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 and 118: (Original face) (Null face) (Range
Page 119 and 120: Table 4.6: Performance of Dual Spac
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: face for each subject, and compare
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
Page 169 and 170: [71] L. Lam and C. Y. Suen. Applica
Page 171 and 172: IEEE Tran. on Pattern Analysis and
Page 173 and 174: of-the-Art Systems. IEEE Tran. on P
Page 175 and 176: and Human Communication, pages 374-
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Master Thesis - Department of Computer Science

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