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Unified probabilistic models for face recognition from a single example image per person

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This paper presents a new technique of unified probabilistic models for face recognition from only one single example image per person. The unified models, trained on an obtained training set with multiple samples per person, are used to recognize facial images from another disjoint database with a single sample per person. Variations between facial images are modeled as two unified probabilistic models: within-class variations and between-class variations. Gaussian Mixture Models are used to approximate the distributions of the two variations and exploit a classifier combination method to improve the performance. Extensive experimental results on the ORL face database and the authors' database (the ICT-JDL database) including totally 1,750 facial images of 350 individuals demonstrate that the proposed technique, compared with traditional eigenface method and some well-known traditional algorithms, is a significantly more effective and robust approach, for face recognition.

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Author information

Correspondence to Pin Liao.

Additional information

Regular Paper

This work is supported partly by the National Natural Science Foundation of China (Grant No.69789301), National Hi-Tech R&D 863 Program of China (Grant No2001AA114190), and Sichuan Chengdu Yinchen Net. Co. (YCNC).

Pin Liao received the B.S. degree in computer science from Nanchang University, Nanchang, P.R. China, in 1996 and the M.S. degree in pattern recognition and intelligent system from beijing Institute of Technology, Beijing, P.R. China, in 1999. He is currently a Ph.D. candidate in Institute of Computing Technology, the Chinese Academy of Sciences. His current research interests include pattern recognition, computer vision, and neural networks.

Li Shen was born in Zhejiang, China, 1937. He graduated from the Department of Electrical Engineering, Zhejiang University, China, in 1959. Since then, he joined the staff of Institute of Computing Technology, the Chinese Academy of Sciences, where he is currently a professor. He is now an IEEE senior member. His research interests include soft computing, ASIC design, design for testability, and fault testing.

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Liao, P., Shen, L. Unified probabilistic models for face recognition from a single example image per person. J. Comput. Sci. & Technol. 19, 383 (2004). https://doi.org/10.1007/BF02944908

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  • pattern recognition
  • face recognition
  • Gaussian mixture model
  • classifier combination
  • unified probabilistic model