Master Thesis - Department of Computer Science

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A.8 (a), (b), (c), (d) and (e) shows blocks of size 16 × 16; (f), (g), (h), (i) and (j) shows the Gabor filters tuned to the orientation and frequency of the corresponding blocks. . . . . . . . . . . . . . . . . . . . . . . . 132 A.9 (a) and (b) shows masks of two input fingerprints shown in Fig. A.2. 132 A.10 (a) and (b) shows the enhanced fingerprint images after masking. . . 133 A.11 (a) The eight directions; (b) The 9×9 mask for computing slit-sums; (c) For ridge pixels, average local intensity or slit-sum is minimum along the direction of the ridge and maximum along the normal direction of ridge. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 A.12 Binarized images of two input fingerprints shown in Fig. A.2. . . . . 135 A.13 The locations of the nine pixels in a window. . . . . . . . . . . . . . 136 A.14 Thinned images of two input fingerprints shown in Fig. A.2. . . . . . 137 A.15 (a)Intra-ridge pixel; (b) Termination minutia; (c) Bifurcation minutia. 138 A.16 Thinned fingerprint images marked with minutiae. . . . . . . . . . . . 139 A.17 Various cases in minutiae matching. (a) Matched Minutiae; (b) Minu- tiae with unmatched angle; (c) and (d) Unmatched minutia. . . . . . 141 xvii
Abstract Biometrics is a rapidly evolving technology, which has been widely used in forensics such as criminal identification, secured access, and prison security. A biometric system is essentially a pattern recognition system that recognizes a person by determining the authenticity of a specific physiological and/or behavioral characteristic possessed by that person. Face is one of the commonly acceptable biometrics used by humans in their visual interaction. The challenges in face recognition stem from various issues such as aging, facial expressions, variations in the imaging environment, illumination and pose of the face. In this thesis, we propose three novel techniques for extraction of facial features and recognition of faces from frontal and near-frontal face images. The principal objective of facial feature extraction is to capture certain discriminative features that are unique for a person. In the first face recognition technique, we propose a new method for representing faces, called as subband face representation. Subject-specific subband face extracts features that are invariant within a subject and at the same time distinguishable across different subjects. This method involves the process of selecting suitable subbands of a face, and then reconstructing it using Inverse Discrete Wavelet Transform (IDWT), based on certain criteria. Subband face representation has been integrated with recent linear subspace analysis techniques to obtain an efficient face recognition system. Other two proposed face recognition techniques deal with two subspaces, namely, range space and null space of within-class scatter which constitute the entire face space if combined. The range space holds the entire intra-class variations and the null space contains the intra-class commonalities present across samples containing variations in expression, illumination and pose. Two of the proposed methods of face recognition combine discriminative features from null space and range space to utilize the whole gamut of discriminative informations present in the face space, using feature fusion and decision fusion. Feature fusion based method uses Gramm-Schmidt Orthonormalization and covariance sum method to
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 and 16: List of Figures 2.1 Summary of appr
Page 17: 3.7 Area difference between genuine
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
Page 67 and 68: 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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