Multi-pose face recognition based on a hierarchical model and fusion decision

Zhu Changren; Wang Runsheng

Volume 24 Issue 11

Nov. 2002

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Article Navigation > Journal of Electronics & Information Technology > 2002 > 24(11): 1447-1453

Zhu Changren, Wang Runsheng. Multi-pose face recognition based on a hierarchical model and fusion decision[J]. Journal of Electronics & Information Technology, 2002, 24(11): 1447-1453.

Citation:

Zhu Changren, Wang Runsheng. Multi-pose face recognition based on a hierarchical model and fusion decision[J]. Journal of Electronics & Information Technology, 2002, 24(11): 1447-1453.

Zhu Changren, Wang Runsheng. Multi-pose face recognition based on a hierarchical model and fusion decision[J]. Journal of Electronics & Information Technology, 2002, 24(11): 1447-1453.

Citation:

Zhu Changren, Wang Runsheng. Multi-pose face recognition based on a hierarchical model and fusion decision[J]. Journal of Electronics & Information Technology, 2002, 24(11): 1447-1453.

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Multi-pose face recognition based on a hierarchical model and fusion decision

Received Date: 2001-03-30
Rev Recd Date: 2001-08-17
Publish Date: 2002-11-19

Abstract

Abstract

A multi-pose face recognition algorithm based on a hierarchical model and fusion decision is proposed in the paper. First, face images are divided into several classes based on their poses and every class into many sub-classes based on their subjects. Then, traditional face recognition technology based on eigenface is applied in the process of face recognition in every class. Finally, fusion decision is adopted to get the final result of face recognition based on the intermediate result of every class. It also, can get the result of its pose recognition and its computation time is far less than that of the traditional methods. The algorithm discussed above has been tested in the experiments of a multi-pose face database with total 100 subjects and 1200 face images, which consists of ORL, UMIST, Stirling and face database of authors lab. The results are encouraging.

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References(1)

References

R. Chellappa, C. L. Wilson, S. Sirohey, Human and machine recognition of faces: A survey, Proc.IEEE., 1995, 83(5), 705-740.[2]A. Samal, P. A. Iyengar, Automatic recognition and analysis of human faces and facial expressions:A survey, Pattern Recognition, 1992, 25(1), 65-77.[3]D. Valentin, H. Abdi, A. J. OToole, G. W. Cottrel, Connectionist model of face processing: A survey, Pattern Recognition, 1994, 27(9), 1209-1230.[4]R. Brunelli, T. Poggio, Face recognition: Feature versus templates, IEEE Trans. on PAMI., 1993,PAMI-15(10), 1042-1052.[5]S. ranganath, Face recognition using transform features and neural networks, Pattern Recognition,1997, 30(10), 1615-1622.[6]Lam Kin-Man, Yan Hong, An analytical-to-holistic approach for face recognition based on a single frontal views, IEEE Trans. on PAMI., 1998, PAMI-20(7), 673-686.[7]M. Turk, A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience, 1991, 3(1),71-86.[8]A. Pentland.[J].B. Moghaddam, T. Starner, View-based and modular eigenspaces for face recognition, IEEE Conf. CVPR94, Seattle.1994,:-[9]K.M. Lam.[J].H. Yan, An improved method for locating and extracting the eye in human face images, ICPR96, Vienna, Austria.1996,:-[10]X. Xie, R. Sudhaker, H. Zhuang, On improving eye feature extraction using deformable templates,Pattern Recognition, 1994, 27(6), 791-799.

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