Master Thesis - Department of Computer Science

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4.5.2 Performance of Dual Space Face Recognition Approach . . . . 96 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5 Enhancing Decision Combination of Face and Fingerprint by Ex- ploitation of Individual Classifier Space: An approach to Multimodal Biometry 100 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 5.2 Theoretical Basis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 5.3 Improving Combined Performance by Strengthening Base Classifiers: Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 5.3.1 The Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6 Conclusion 118 6.1 Contribution of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . 118 6.2 Scope of Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 Appendices 121 A Fingerprint Recognition System 122 A.1 Image Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 A.1.1 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 A.1.2 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 A.1.3 Orientation Image . . . . . . . . . . . . . . . . . . . . . . . . 125 A.1.4 Ridge Frequency Image . . . . . . . . . . . . . . . . . . . . . . 127 A.1.5 Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 A.2 Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 A.3 Image Binarization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 A.4 Image Thinning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 A.5 Minutiae Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 ix
A.6 Minutiae Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 A.6.1 Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 A.6.2 Minutia Pairing . . . . . . . . . . . . . . . . . . . . . . . . . . 141 A.6.3 Matching Score Computation . . . . . . . . . . . . . . . . . . 142 A.7 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 Bibliography 143 x
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: 3 An Efficient Method of Face Recog
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
Page 59 and 60: 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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