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báo cáo hóa học:" Research Article Evolutionary Discriminant Feature Extraction with Application to Face Recognition"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Evolutionary Discriminant Feature Extraction with Application to Face Recognition | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 465193 12 pages doi 10.1155 2009 465193 Research Article Evolutionary Discriminant Feature Extraction with Application to Face Recognition Qijun Zhao 1 David Zhang 1 Lei Zhang 1 and Hongtao Lu2 1 Biometrics Research Centre Department of Computing Hong Kong Polytechnic University Hong Kong 2 Department of Computer Science Engineering Shanghai Jiao Tong University Shanghai 200030 China Correspondence should be addressed to Lei Zhang cslzhang@comp.polyu.edu.hk Received 27 September 2008 Revised 8 March 2009 Accepted 8 July 2009 Recommended by Jonathon Phillips Evolutionary computation algorithms have recently been explored to extract features and applied to face recognition. However these methods have high space complexity and thus are not efficient or even impossible to be directly applied to real world applications such as face recognition where the data have very high dimensionality or very large scale. In this paper we propose a new evolutionary approach to extracting discriminant features with low space complexity and high search efficiency. The proposed approach is further improved by using the bagging technique. Compared with the conventional subspace analysis methods such as PCA and LDA the proposed methods can automatically select the dimensionality of feature space from the classification viewpoint. We have evaluated the proposed methods in comparison with some state-of-the-art methods using the ORL and AR face databases. The experimental results demonstrated that the proposed approach can successfully reduce the space complexity and enhance the recognition performance. In addition the proposed approach provides an effective way to investigate the discriminative power of different feature subspaces. Copyright 2009 Qijun Zhao et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use .