Master Thesis - Department of Computer Science

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3.1 (a) Block diagram for generating subband face. The face image is de- composed using Discrete Wavelet Transform (DWT) into subbands. Selected subbands are used to reconstruct the subband face using inverse DWT. (b) Subband faces are projected onto a lower dimension featurespace using PCA or LDA (or their variants) and matched using the nearest neighbor strategy. . . . . . . . . . . . . . . . . . . . . . . 47 3.2 Two dimensional discrete wavelet transform (DWT) for an image: The low pass filter h(.) and the high pass filter g(.) are applied along the rows initially followed by downsampling by a factor of two. This is followed by filtering using h(.) and g(.) along the columns and down- sampled by factor of two. . . . . . . . . . . . . . . . . . . . . . . . . 49 3.3 (a) Original face, (b) the level-1 wavelet decomposition of the face image into subbands A, H (top) and V , D (bottom) and (c) level- 3 dyadic wavelet decomposition. All subband signals are normalized individually. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.4 Two dimensional inverse discrete wavelet transform (IDWT) for an image. The rows are initially upsampled by factor of two and filtered using low pass h(.) and high pass g(.) filters. This is followed by upsam- pling along columns by a factor of two and applying the low pass h(.) and high pass g(.) filters along the columns to get the reconstructed image. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.5 Reconstruction of a subband face using two dimensional Inverse Dis- crete Wavelet Transform (IDWT) from level-2 subbands. The approximation at level-2, LL2, is suppressed (by replacing with zeros) while reconstructing the approximation at level-1, LL1. This is further used to generate the subband face at level-0. . . . . . . . . . . . . . . . . . 52 3.6 (a), (b), (c): Typical examples of sample face images from the Yale, PIE and ORL face databases. The respective subband faces (a1-c1), (a2-c2) and (a3-c3) with the approximations at level-4, level-5 and level-6 suppressed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 xv
3.7 Area difference between genuine and impostor distribution with their ideal counterparts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.8 ZeroFRR and TZeroF RR are illustrated using hypothetical curves of FAR(t) and FRR(t). . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.9 Training samples for three databases: first, second and third row shows the training set for Yale, PIE, and ORL databases, respectively. . . . 64 4.1 A typical eigenvalue spectrum with its division into null space and range space. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.2 Stepwise feature subset selection . . . . . . . . . . . . . . . . . . . . . 80 4.3 Architecture of our proposed method of feature fusion. . . . . . . . . 82 4.4 The fusion architecture of the proposed method of decision fusion. . . 87 4.5 Examples where LDA based on the criteria tr(S −1 w Sb) fails to discrim- inate between classes. . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.6 Unique decomposition of faces into null space and range space of within- class scatter drawn from Yale (first row), ORL (second row) and PIE (third row) databases. . . . . . . . . . . . . . . . . . . . . . . . . . . 97 4.7 Training samples for three databases: First, second and third row shows the training set for Yale, ORL, PIE databases, respectively. . 98 5.1 Operation of proposed method for classifier fusion. . . . . . . . . . . . 108 A.1 (a) and (b) A local window containing ridge valley structures; (c) and (d) Curve (i.e. x-signature) obtained along the direction normal to the ridge orientation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 A.2 Input fingerprint images. . . . . . . . . . . . . . . . . . . . . . . . . . 125 A.3 Normalized images of two input fingerprints shown in Fig. A.2. . . . 125 A.4 Orientation images of two input fingerprints shown in Fig. A.2. . . . . 127 A.5 A X-signature with w = W and l = H in our case. . . . . . . . . . . . 127 A.6 Frequency images (after filtering) of two input fingerprints shown in Fig. A.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 A.7 Enhanced images of two input fingerprints shown in Fig. A.2. . . . . 131 xvi
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 and 12: A.6 Minutiae Matching . . . . . . .
Page 13 and 14: 4.1 Effect of increasing number of
Page 15: List of Figures 2.1 Summary of appr
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.
Page 61 and 62: Class-indifferent Methods • Decis
Page 63 and 64: 3. The final DS soft vector is calc
Page 65 and 66: 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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