Master Thesis - Department of Computer Science

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(a) cn(p) = 2 (b) cn(p) = 1 (c) cn(p) = 3 Figure A.15: (a)Intra-ridge pixel; (b) Termination minutia; (c) Bifurcation minutia. • 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 For each extracted minutiae point, the following information is recorded: • x and y coordinates, • orientation, α, of the associated ridge segment, and • type of the minutiae (ridge ending or bifurcation). In the second stage, there are 3-4 levels of elimination: nearby features, L-shaped false bifurcations, boundary effects and spike elimination. • Ridge break elimination: Two end points with the same orientation and within a distance threshold T1 are eliminated. • Spike elimination: An end point which is connected to a bifurcation point and is also within a distance threshold T2 is eliminated. • Boundary effects: The minutiae detected within a specified border of the boundary of the foreground areas are deleted. The post-processing stage eliminates spurious feature points based on the structural and spatial relationships of the minutiae. In Fig. A.16, the minutiae features after post-processing, for two input fingerprint images are shown. Bifurcations are marked 138
(a) (b) Figure A.16: Thinned fingerprint images marked with minutiae. by square box and ridge endings by a ”+” sign. Minutiae directions are marked with an arrow (→) sign. A.6 Minutiae Matching The matching is done using Elastic graph matching as proposed by Ratha et al. [100]. The output of this stage is a matching score which says the goodness of the matching. There are three main steps involved in this process: 1. Registration, 2. Minutiae Pairing, and 3. Matching Score Computation. A.6.1 Registration In order to match two point sets with unknown orientation, scale, and translation, the two point sets must be registered with respect to each other. The orientation, scale, and translation parameters are estimated using a generalized Hough Transform [8]. The input to the registration algorithm consists of two sets of minutiae points P and Q extracted from two fingerprint images. They are denoted by, P = � (p 1 x, p 1 y, α 1 ), ....., (p P x , p P y , α P ) � , 139
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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
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1.1.1 Applications Biometrics has b
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• Spoof Attacks: An impostor may
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• A new approach for multimodal b
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CHAPTER 2 Literature Review This re
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ange [23], infrared scanned [137] a
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u 2 x 2 u 1 x 1 (a) PCA basis (b) P
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PCA Projection Class 1 Class 2 LDA
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F DIFS DFFS F (a) (b) F 1 L Figure
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image A of m rows and n columns is
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faces of 40 subjects. • Others: A
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malizing the vector components, the
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Figure 2.5: A fingerprint image wit
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features (gradient coherence, inten
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Figure 2.7: Examples of minutiae; A
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according to the local orientation
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• Parallel Mode: This operational
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can address the problem of noisy se
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Prior to Matching Sensor Level Feat
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determined by logistic regression.
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Class-indifferent Methods • Decis
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3. The final DS soft vector is calc
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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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Page 115 and 116: Table 4.3: Effect of increasing num
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Page 121 and 122: 5.1 Introduction In recent years, t
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Page 125 and 126: validation1, validation2 and test.
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Page 145 and 146: (a) (b) Figure A.2: Input fingerpri
Page 147 and 148: (a) (b) Figure A.4: Orientation ima
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Page 151 and 152: (a) (b) Figure A.7: Enhanced images
Page 153 and 154: (a) (b) Figure A.10: (a) and (b) sh
Page 155 and 156: (a) (b) Figure A.12: Binarized imag
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Page 161 and 162: Paired Minutiae Minutiae with unmat
Page 163 and 164: Bibliography [1] FVC 2002 website.
Page 165 and 166: [25] Li-Fen Chen, Hong-Yuan Mark Li
Page 167 and 168: [49] Lin Hong, Yifei Wan, and Anil
Page 169 and 170: [71] L. Lam and C. Y. Suen. Applica
Page 171 and 172: IEEE Tran. on Pattern Analysis and
Page 173 and 174: of-the-Art Systems. IEEE Tran. on P
Page 175 and 176: and Human Communication, pages 374-
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Master Thesis - Department of Computer Science

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