List of Publications The following is the paper published during the work done on this thesis, the text of which is included in part in the thesis: 1. A. P. James and S. Dimitrijev, “Face recognition using local binary decisions,” IEEE Signal Processing Letters, vol. 15, 2008, pp. 821-824. Acknowledgement This thesis

) to alleviate facial variations for face recognition. Finally, our experimental results show that FSVDR can: 1) effectively alleviate facial variations; and 2) form an intermediate representation for many FR methods such as PCA and LDA to significantly improve their classification performance in case of great

recognition, many methods are used but due to advancement there are some new methods and algorithm used for recognition of face i.e. line edges map, support vector machine etc. for face recognition. A number of current face recognition algorithms use face representations found by supervised and unsupervised

. For practical applications, this representation has to be realized by structured transforms and fast algorithms. Recently, it has become evident that commonly used separable transforms (such as wavelets) are not necessarily best suited for images. Thus, there is a strong motivation to search for more powerful

Face recognition has been an important issue in computer vision and pattern recognition over the last several decades (Zhao et al., 2003). While human can recognize faces easily, automated face recognition remains a great challenge in computer-based automated recognition research. One difficulty

, it constituted a strong ensemble based KDCV framework by taking advantage of boosting and KDCV techniques. Conclusion: This new method was applied on extended yale B face database and achieves better classification accuracy. Experimental results demonstrated the promising performance of the proposed method

In this thesis, we propose an algebraic framework for developing classifiers for application in the context of face recognition. We use this algebraic framework to develop classifiers based on the classical Procrustes analysis and its variants and also on the Moore-Penrose inverse. We also propose

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, 3D face reconstruction methods cannot provide dynamic shape and appearance information at full video rate. The contribution of this work is the development of a novel multi-view 3D capture system which simultaneously acquires dynamic face shape and appearance at full video rate. Optical filters

In this paper, a framework of Discriminant Subspace Analysis (DSA) method is proposed to deal with the Small Sample Size (SSS) problem in face recognition area. Firstly, it is rigorously proven that the null space of the total covariance matrix, St, is useless for recognition. Therefore, a