Master Thesis - Department of Computer Science

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x1 xi xL D1 Di DL DP (x) D1(x) Di(x) DL(x) k1 Bi ki Bi kL BL ¯ DP (x) H1 Hi HL Sum Rule ˜D(x) Maximum Membership Rule Crisp Class Label Figure 5.1: Operation of proposed method for classifier fusion. T S 2. Use all of RVi , where i = 1, 2, .., L, to construct set of DP’s for test samples. Now, for a test sample x, ¯ DP (x) = [H1(x), H2(x), ..., HL(x)] T , where H can be D or ¯ D based on the performance of the classifier observed in 1.3. 3. Use sum rule on ¯ DP (x) to get soft class label vector ˜ D(x). 4. Maximum or minimum (based on the implementation of min-max normaliza- tion technique) membership rule on ˜ D(x) will provide crisp class label for x. The architecture illustrating our proposed methodology of decision fusion is given in Fig 5.1. The blocks labeled Bi’s i=1,2,..,L, use a binary variable ki, i = 1, 2, ..., L for decision-making purpose. When ki = 1 (the LDA-based eigenmodel is beneficial), the output of classifier Di(x) on test sample x is replaced by ¯ D(x) in DP(x), while ki = 0 ensures that output Di(x) is passed on as the output unaltered. In the diagram ¯ DP (x) represents modified DP (x) after replacing it’s rows Di(x) by ¯ Di(x) conditionally (see step 2 of the algorithm). A comparative study of the performance of the proposed technique of decision fusion using class-specific information for a classifier, with that of those in existing literature, is given in the following section. The decision fusion techniques used for comparison in our experimentation are described in section 2.3.3.3. 108
5.4 Experimental Results In this chapter, we have combined face and fingerprint features at decision level. We have used PIE [5] and ORL [3] databases for face. We have labeled F PA and F PB as fingerprint databases acquired from the FVC (Fingerprint Verification Competition) 2002 [1] and 2004 website [2]. Both the fingerprint databases have 10 subjects with 8 images per subject. So we have selected 10 persons from both PIE and ORL databases with 42 and 10 images per subject respectively. Assuming that face and fingerprint are statistically independent for an individual, we have associated an individual from face database with an individual from fingerprint database to create a virtual subject. Now each database is divided into three disjoint sets namely train set, validation set and test set. Table 5.1 and Table 5.2 show the distribution of images per subject for the train, validation and test sets for face and fingerprint respectively, as used in our experimentation. Table 5.1: Sample distribution (per subject) in train, validation and test sets for face databases. PIE ORL Set T est1 T est2 T est1 T est2 Train 4 4 1 1 Validation 4 4 3 3 Test 34 34 6 6 Table 5.2: Sample distribution (per subject) in train, validation and test sets for fingerprint databases. F PA or F PB Set T est1 T est2 T est3 T est4 Train 1 1 1 1 Validation 2 3 3 4 Test 5 4 4 3 109
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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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Page 83 and 84: Table 3.3: Peak Recognition Accurac
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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 99 and 100: Thus, calculation of the QR factori
Page 101 and 102: 4.3.3 Algorithm for Feature Fusion
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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
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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
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Master Thesis - Department of Computer Science

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