Master Thesis - Department of Computer Science

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the main problem; whereas the problems for a machine face recognition system are: 1. Facial expression change 2. Illumination change 3. Aging 4. Pose change 5. Scaling factor (i.e. size of the image) 6. Frontal vs. profile 7. Presence and absence of spectacles, beard, mustache etc. 8. Occlusion due to scarf, mask or obstacles in front. The problem of automatic face recognition (AFR) is a composite task that involves detection of faces from a cluttered background, facial feature extraction, and face identification. A complete face recognition system has to solve all subproblems, where each one is a separate research problem. This research work concentrates on the problem of facial feature extraction and face identification. Most of the current face recognition algorithms can be categorized into two classes, image template based and geometry feature-based. The template based methods [9] compute the correlation between a face and one or more model templates to estimate the face identity. Brunelli and Poggio [16] suggest that the optimal strategy for face recognition is holistic and corresponds to template matching. In their study, they compared a geometric feature based technique with a template matching based system and reported an accuracy of 90% for the first one and 100% for the second one on a database of 97 persons. Statistical tools such as Support Vector Machines (SVM) [92, 127], Principal Component Analysis (PCA) [114, 124], Linear Discriminant Analysis (LDA) [12], kernel methods [109, 136], and neural networks [50, 74, 97] have been used to construct a suitable set of face templates. Other than statistical analysis and neural network approach there are other approaches known as hybrid approaches which use both statistical pattern recognition techniques and neural network systems. Examples for hybrid approaches include the combination of PCA and Radial Basis Function (RBF) neural network [37, 122]. Among other methods, people have used 10
ange [23], infrared scanned [137] and profile [79] images for face recognition. While templates can be viewed as features, they mostly capture global features of the face image. Facial occlusion is often difficult to handle in these approaches. The geometry feature based methods analyze explicit local facial features, and their geometric relationships. Cootes et al. [72] have presented an active shape model in extending the approach by Yuille [139]. Wiskott et al. [131] developed an elastic bunch graph matching algorithm for face recognition. Penev et al. [95] developed PCA into Local Feature Analysis (LFA). This technique is the basis for one of the most successful commercial face recognition systems, FaceIt. The summary of approaches to face recognition is shown in Fig. 2.1. 2.1.1 Template based Methods Template matching is conceptually related to holistic approach which attempts to identify faces using global representations [51]. These type of methods approach the face image as a whole and try to extract features from the whole face region and then classify the image by applying a pattern classifier. One of the methods used to extract features in a holistic system, is based on statistical approaches which are discussed in the following section. 2.1.1.1 Statistical Approaches Images of faces, represented as high-dimensional pixel arrays, often belong to a manifold of intrinsically low dimension. Face recognition research has witnessed a growing interest in techniques that capitalize on this observation, and apply algebraic and statistical tools for extraction and analysis of the underlying manifold. The techniques that identify, parameterize and analyze linear subspaces are described below. Other than linear subspaces there are some statistical face recognition techniques which are based on nonlinear subspaces (like kernel-PCA and kernel-LDA), transformation (like DCT, DCT & HMM and Fourier Transform) and Support Vector Machine (SVM). Appearance-based approaches for face recognition like PCA, LDA, and proba- bilistic subspace view a 2D face image as a vector in image space. A set of face images {xi} can be represented as a M × N matrix X = [x1, x2, ..xi, ...., xN], where 11
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: CHAPTER 2 Literature Review This re
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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
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Page 51 and 52: according to the local orientation
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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 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 = |
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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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