Master Thesis - Department of Computer Science

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CHAPTER 3 An Efficient Method of Face Recognition using Subject-Specific Subband Faces This chapter presents an efficient method for frontal face recognition, using subject- specific subband face representation. The human face has certain visual features that are common among everybody and some others that exhibit the unique characteris- tics of an individual. Using the discrete wavelet transform (DWT), we extract these unique features from the face image for discriminating it from others. The face image is decomposed into several subbands to separate the common (approximation) and discriminatory (detail) parts. Subband face is reconstructed from selected wavelet subbands, in which a suitable approximation subband is 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 on several linear subspace techniques: PCA, LDA, 2D-PCA, 2D-LDA and Discriminative Common Vectors (DCV) with Yale, ORL and PIE face databases shows that the subband face 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. The rest of the chapter is organized as follows. Section 3.1 provides the motivation and a brief introduction to our proposed method along with a concise overview on wavelet based face recognition techniques. The Wavelet Decomposition and the method to reconstruct a subband face is de- scribed in section 3.2. Section 3.3 highlights over the criteria used as cost functions and also the algorithm to select optimum subject-specific subbands. Section 3.4 dis- cusses the experimental results of the proposed subband face representation tested
on three face databases and compared with the state-of-art closest competitor [36] of it. Section 3.5 concludes the chapter. 3.1 Introduction and Motivation How do humans identify individuals with remarkable ease and accuracy? This ques- tion has haunted psychologists, neurologists and, recently, engineers in biometry for a long time. The human face is different from any other natural or man-made objects, but has similar structural features across different races, sex and regions. The subtle variations in the face structure are captured by the human brain and help in discriminating one face from the another. The human brain is able to filter out the common visual features of a face and retain only those suitable to exhibit the unique charac- teristics (discriminatory evidence) of an individual. An efficient face-based biometric system must possess these properties (as in those in the retina and visual cortex), to perform efficiently like a human being. Wavelet-based features have already been used to obtain a better face representation in [36, 40, 26, 140]. The use of wavelet packet for face recognition was reported in [40]. Here, 2D-DWT is used to fully decompose the face image and simple statistical features such as mean and variance are extracted from the decomposed coefficients, and used as a feature vector for representation. The use of wavelet subband representation along with kernel associative memory for face recognition was reported in [140]. Here wavelet decomposed faces are used to build an associative memory model for each class and kernel methods are used to exploit higher order relations which cannot be captured by linear transformations. Face images with illumination variations can be modeled by low-dimensional linear subspaces. The existence of single light source directions that are effective for face recognitions was discussed in [75]. Ekenel [36] et al. proposed multiresolution face recognition with fusion at data, feature and decision levels to test on face images that differ in expression or illumination separately, obtained from CMU PIE, FERET and Yale databases. Significant performance gains are reported against illumination perturbations. They selected a number of subbands on the basis of performance on testing set and termed them as successful subbands. Data, feature and decision level fusion are performed on these successful subbands to 45
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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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Page 19 and 20: Abstract Biometrics is a rapidly ev
Page 21 and 22: CHAPTER 1 Introduction The issues a
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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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