Master Thesis - Department of Computer Science

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ZEROS LH2 HL2 HH2 2 2 2 Rows h(.) g(.) h(.) 2 g(.) 2 2 Columns h(.) g(.) 2 h(.) LH1 2 g(.) HL1 HH1 2 2 Rows h(.) g(.) 2 Columns h(.) 2 g(.) Subband face image Figure 3.5: Reconstruction of a subband face using two dimensional Inverse Discrete 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. subband at a larger level of decomposition leads to reconstruction of a face that is similar to the original gray level image. Let the original face image, at the root of the decomposition tree, be A0 and the approximations at different levels of decomposition be denoted by A1, A2 and so on. The subband face that is reconstructed with the approximation suppressed at level-i be denoted as (A0 − Ai). In [94], it was shown that the subband face reconstructed (using Haar transform) with the approximation suppressed at level-5, (A0 − A5), gives the best performance for PCA based face recognition for Yale database. In this chapter, we propose a method of implementation to obtain a subband face, (Al −Ak), which is reconstructed to the level-l, after suppressing the approximation at level- k, where 0 ≤ l < k ≤ log N, N ∗ N being the resolution of the image. Note that this eliminates the details: Vi, Hi, Di, for levels i = 1, 2, . . . , l when l > 0, from the original face image. It was found that for optimal performance of face recognition, the levels of decomposition l and k depend on two factors: • Subject under consideration, • Linear subspace method used for face recognition 52
(a) (a1) (a2) (a3) (b) (b1) (b2) (b3) (c) (c1) (c2) (c3) Figure 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. In the following section, we explain and describe the algorithm for selecting subject- specific optimal subbands and the criteria used for searching optimal values of l and k to generate the best subband face. 3.3 Proposed Methods for Subject-Specific Subband Selection In this section, we will describe four different criteria and the algorithm used for subject-specific subband selection. As every person has a unique facial structure with respect to others, self-shadowing and other factors such as outline, appear- ance etc. provide a strong basis for the existence of a separate subband (Ali − Aki ) for each person. Precisely, the output of subband selection algorithm will be C (li, ki) pairs, where i = 1, 2..., C, C being the number of subjects. We calculate two measures called goatishness and lambishness [34] for each subject and for each l and k pair where l and k varies from l = 0, 1, ..., L and k = (l + 1), ....., K. The 53
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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
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
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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
Page 69 and 70: Image Rows h(.) g(.) 2 2 Columns Fi
Page 71: in the accuracy of face recognition
Page 75 and 76: Genuine Impostor Scores Scores 1 0
Page 77 and 78: Error d1 = (1 − TZeroF RR) d2 = |
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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
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Page 107 and 108: x DNull DRange DP (x) DNull(x) DRan
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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
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Page 119 and 120: Table 4.6: Performance of Dual Spac
Page 121 and 122: 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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