Master Thesis - Department of Computer Science

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suppressed and a detail subband (in some cases) is eliminated. Reconstructing a face with an optimal selection of subbands enhances the performance of face recognition. We present four different criteria as cost functions to obtain an optimal subband face for each subject, and compare their performances. The performance of the subband face representation with several linear subspace techniques: PCA, LDA, 2D-PCA, 2D-LDA and Discriminative Common Vec- tors (DCV), on Yale, ORL and PIE face databases show that the subband face based representation performs significantly better than that proposed by Ekenel for Multiresolution Face Recognition [36] for frontal face recognition, in the presence of varying illumination, expression and pose. • In the second approach, we propose a new face recognition technique by combining information from null space and range space of within-class scatter of a face space. The combination of information at feature level poses a problem of optimally merging two eigenmodels obtained separately from null space and range space. We use two different methods: 1) Covariance Sum and 2) Gramm- Schmidt Orthonormalization to construct a new combined space, named as dual space, by merging two different sets of discriminatory directions obtained from null space and range space separately. We employ forward or backward selection technique to select the best set of discriminative features from dual space and use them for face recognition. Experimental results on three public databases, Yale, ORL and PIE will show the superiority of our method over a face recognition technique called Discriminative Common Vectors (DCV) [20], based on only the null space of within-class scatter. • The third approach of face recognition presents a novel face recognition technique by combining information from null space and range space of within-class scatter of a face space at decision level. Along with two classical decision fusion strategies sum rule and product rule, we employ our own decision fusion technique which exploits each classifier space separately to enhance combined performance. Our method of decision fusion uses Linear Discriminant Anal- ysis (LDA) and nonparametric LDA on classifier’s response to enhance class separability. This method is also evaluated using Yale, ORL and PIE database. 6
• A new approach for multimodal biometry has been proposed based on a decision fusion technique to combine decisions from face and fingerprint classifiers. This process of decision fusion exploits the individual classifier space on the basis of availability of class specific information present in the classifier output space. The class specific contextual information is exploited using LDA and nonparametric LDA at the classifier response level to enhance class separability. Eventhough, face classifier is observed to provide contextual information, fingerprint classifier often does not provide this information due to high sensi- tivity of available minutiae points, producing partial matches across subjects. The enhanced face and fingerprint classifiers are combined using sum rule. We also propose a generalized algorithm for Multiple Classifier Combination (MCC) based on our approach. Experimental results exhibit the superiority of the proposed method over other existing fusion techniques like sum, product, max, min rules, decision template and Dempster-Shafer theory. 1.4 Overview of the Thesis In this thesis, the problem of face recognition has been attempted using three different techniques and then face and fingerprint information are combined to construct a multimodal biometric system. The rest of the thesis is organized in the following way. Chapter 2: Literature Review - This chapter discusses the techniques and recent developments in the field of face, fingerprint, and multiple classifier combination. A few methods based on linear transformation for face recognition are also described in this chapter. We also provide a vivid description of a set of methods for Multiple Classifier Combination (MCC). Chapter 3: An Efficient Method of Face Recognition using Subject-Specific Subband Faces - This chapter emphasizes on our proposed method of face recognition using Subband face representation. We propose four criteria and an algorithm for obtaining subject-specific subband faces and then integrate them separately with five linear subspace methods, namely, PCA, LDA, 2D-PCA, 2D-LDA and DCV. Results 7
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 . . . . . . .
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Page 15 and 16: List of Figures 2.1 Summary of appr
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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
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Page 29 and 30: CHAPTER 2 Literature Review This re
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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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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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