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Báo cáo hóa học: " Face Recognition Using Local and Global Features"

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: Face Recognition Using Local and Global Features | EURASIP Journal on Applied Signal Processing 2004 4 530-541 2004 Hindawi Publishing Corporation Face Recognition Using Local and Global Features Jian Huang Department of Computer Science Hong Kong Baptist University Kowloon Tong Hong Kong Email jhuang@comp.hkbu.edu.hk Pong C. Yuen Department of Computer Science Hong Kong Baptist University Kowloon Tong Hong Kong Email pcyuen@comp.hkbu.edu.hk J. H. Lai Department of Mathematics Zhongshan University Guangzhou 510275 China Email stsljh@zsulink.zsu.edu.cn Chun-hung Li Department of Computer Science Hong Kong Baptist University Kowloon Tong Hong Kong Email chli@comp.hkbu.edu.hk Received 30 October 2002 Revised 24 September 2003 The combining classifier approach has proved to be a proper way for improving recognition performance in the last two decades. This paper proposes to combine local and global facial features for face recognition. In particular this paper addresses three issues in combining classifiers namely the normalization of the classifier output selection of classifier s for recognition and the weighting of each classifier. For the first issue as the scales of each classifier s output are different this paper proposes two methods namely linear-exponential normalization method and distribution-weighted Gaussian normalization method in normalizing the outputs. Second although combining different classifiers can improve the performance we found that some classifiers are redundant and may even degrade the recognition performance. Along this direction we develop a simple but effective algorithm for classifiers selection. Finally the existing methods assume that each classifier is equally weighted. This paper suggests a weighted combination of classifiers based on Kittler s combining classifier framework. Four popular face recognition methods namely eigenface spectroface independent component analysis ICA and Gabor jet are selected for combination and three popular face databases namely Yale database Olivetti .