Master Thesis - Department of Computer Science

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(a) cn(p) = 2 (b) cn(p) = 1 (c) cn(p) = 3 Figure 2.8: (a) Intra-ridge pixel; (b) Termination minutia; (c) Bifurcation minutia number different from 2. The crossing number [6] cn(p) of a pixel p in a binary image is defined as half the sum of the differences between pairs of adjacent pixels in the 8-neighborhood of p: cn(p) = 1 2 � i=1,..,8 |val(pi mod 8) − val(pi−1)|. (2.20) where p0, . . . , p7 are the pixels belonging to an ordered sequence of pixels defining the 8-neighborhood of p and val(p) ∈ {0, 1} is the pixel value. It is simple to note (Fig. 2.8) that for a pixel p with val(p) = 1, it: • is an intermediate ridge point if cn(p) = 2; • corresponds to a termination minutia if cn(p) = 3; • defines a more complex minutia (bifurcation, crossover, etc.) if cn(p) ≥ 3 Leung et al.’s [76] method extracts the minutiae from thinned binary images but, instead of using the crossing number, they use a three-layer perceptron neural net- work. In some approaches, minutiae are detected from binary images without any intermediate thinning. Weber [129] first performed an image enhancement by band- pass filtering in the frequency domain and then did binarization via local threshold. Finally, the minutiae are detected from the thick binary ridges through a ridge track- ing algorithm. Szekély and Szekély [121] developed a minutiae detection technique based on the computation of the orientation image divergence. Miao and Maltoni [82] proposed a direct gray-scale minutiae extraction algorithm, where the basic idea was to tract the ridge lines in a gray-scale image by “sailing”, 30
according to the local orientation of the ridge pattern. From a mathematical point of view, a ridge line is defined as a set of points that are local maxima relative to a section orthogonal to the ridge direction. By connecting the consecutive maxima, polygonal approximation of the ridge line can be obtained. 2.2.3 Fingerprint Matching A fingerprint matching algorithm compares two given fingerprints and returns either a degree of similarity (without loss of generality, a score between 0 and 1) or binary decision (matched/non-matched). Matching fingerprint images is an extremely diffi- cult problem, mainly due to the large variability in different impressions of the same finger (i.e. large intra-class variations). The main factors responsible for intra-class variations are: • Displacement • Rotation • Partial overlap • Non-linear distortion • Pressure and skin condition • Noise The large number of approaches to fingerprint matching can be coarsely classified into three families: 1) Correlation-based, 2) Minutiae-based and 3) Ridge feature-based. In pattern recognition literature, the minutiae matching problem has been generally addressed as point pattern matching problem. Even though a small difference exists due to the presence of a direction associated with each minutia point, the two problems may be approached analogously. For point pattern matching there are a family of approaches: relaxation methods, algebraic and operational research solutions, tree- pruning approaches, energy-minimization methods, Hough transforms, and so on. The relaxation approach [104, 99] iteratively adjusts the confidence level of each corresponding pair of points based on its consistency with other pairs until a certain criterion is satisfied. Hong and Tan [47], and Sprinzak and Werman [116] proposed 31
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 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: Figure 2.7: Examples of minutiae; A
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
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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 and 74: (a) (a1) (a2) (a3) (b) (b1) (b2) (b
Page 75 and 76: Genuine Impostor Scores Scores 1 0
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
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
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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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