Master Thesis - Department of Computer Science

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maximum values of l and k (L and K, respectively) depend on original image size and the subspace method used. The terms Goat and Lamb originated from speaker verification [34] and then prop- agated in the field of biometry [107]. Goats are the group of subjects that are hard to authenticate and generate the majority of false rejects. The group of subjects that are easy to imitate and cause false acceptance are regarded as Lambs. Appro- priate subband for a person gathers all common intra-class detail features specific to a subject as well as eliminates the common inter-class structural features present across subjects. So, it can be stated that a proper subband face has the power to extract features that are invariant within a subject and at the same time discriminant across different subjects. Goatishness and labmishness measures for each person are calculated from the genuine and impostor distributions obtained from the confusion matrix on a validation set. Formally, let n and p be the number of samples per subject used for training and validation, respectively. The columns and rows of a confusion matrix correspond to the training and validation samples for different classes arranged sequentially. So a confusion matrix (CM) can be organized as follows, ⎡ m(11)(11) ⎢ . . . m(11)(1j) . . . m(11)(1n) . . . m(11)(ij) . . . m(11)(Cn) ⎢ . . . . . . . . . . . . . . . . . . . . . . . . . . . ⎢ m(1s)(11) . . . m(1s)(1j) . . . m(1s)(1n) . . . m(1s)(ij) . . . m(1s)(Cn) ⎢ . . . . . . . . . . . . . . . . . . . . . . . . . . . ⎢ CM = ⎢ m(1p)(11) . . . m(1p)(1j) . . . m(1p)(1n) . . . m(1p)(ij) . . . m(1p)(Cn) ⎢ . . . . . . . . . . . . . . . . . . . . . . . . . . . ⎢ m(rs)(11) . . . m(rs)(1j) . . . m(rs)(1n) . . . m(rs)(ij) . . . m(rs)(Cn) ⎢ . . . . . . . . . . . . . . . . . . . . . . . . . . . ⎣ m(Cp)(11) . . . m(Cp)(1j) . . . m(Cp)(1n) . . . m(Cp)(ij) . . . m(Cp)(Cn) (3.1) An element m(rs)(ij) represents the similarity measure (score value) between s th validation sample from class r and j th training sample from class i and takes value in the range of [0, 1]. Now the genuine and impostor score sets for class i, denoted as 54 ⎤ ⎥ ⎦
Genuine Impostor Scores Scores 1 0 1 0 No of Samples GD b 1 No of Samples (a) Figure 3.7: Area difference between genuine and impostor distribution with their ideal counterparts. GDi and IDi, can written as, GDi = {m(rs)(ij) | i = r , j = 1, 2, ..., n and s = 1, 2, ..., p} (3.2) IDi = {m(rs)(ij) | i �= r, r = 1, 2, ..., C, j = 1, 2, ..., n and s = 1, 2, ..., p} (3.3) We have formulated four different criteria for measuring goatishness and lambishness for a person. Ideally, all elements of the confusion matrix for a genuine subject should be one (1) whereas, elements for impostor subjects should be zero (0). We have tried to measure the amount of deviations of genuine and impostor distributions from their ideal counterparts. The criteria need to be minimized for choosing appropriate subbands. The criteria are described below: 3.3.1 Criteria used for measuring Goatishness and Lambishness • First Criterion (C1): Let the number of elements in the genuine and impostor sets be b GD = n ∗ p and b ID = n ∗ (C − 1) ∗ p, respectively irrespective of any subject. Taking the elements along x-axis, if we plot the scores of the genuine samples we obtain a multimodal hill-shaped curve (genuine distribution). The difference in area between this and the ideal curve gives the measure of deviation for genuine distribution from its ideal counterpart. For impostor distribution, the measure is the area under impostor curve (see Figure 3.7). So goatishness and lambishness measures for i th subject denoted as Gi and Li, 55 1 (b) ID b
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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
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
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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 and 72: in the accuracy of face recognition
Page 73: (a) (a1) (a2) (a3) (b) (b1) (b2) (b
Page 77 and 78: Error d1 = (1 − TZeroF RR) d2 = |
Page 79 and 80: The algorithm for obtaining the sub
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
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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
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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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