›› 2010, Vol. 25 ›› Issue (3): 641-650.

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Pattern Recognition and Image Processing • Previous Articles    

A New Classifier for Facial Expression Recognition: Fuzzy Buried Markov Model

Yong-Zhao Zhan (詹永照), Senior Member, CCF, Ke-Yang Cheng (成科扬), Member, CCF, Ya-Bi Chen (陈亚必), and Chuan-Jun Wen (文传军)   

  1. School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang 212013, China
  • Received:2009-08-25 Revised:2010-01-30 Online:2010-05-05 Published:2010-05-05
  • About author:
    Yong-Zhao Zhan received his M.S. degree in computer science from Jiangsu University, China, and his Ph.D. degree in computer science from Nanjing University, China, in 1990 and 2000 respectively. From October 1995 to October 1996, he was at Konstanz University of Technology, Germany, as a visiting scholar. He is currently a professor of School of Computer Science and Telecommunication Engineering at Jiangsu University. His research interests include human computer interaction, pattern recognition, and multimedia technology. He is a senior member of CCF.
    Ke-Yang Cheng is a member of CCF. He received the M.S. degree from the School of Computer Science and Telecommunication Engineering of Jiangsu University in 2008. He has co-authored more than 20 journal and conference papers. He is currently a researcher and teaching assistant in the School of Computer Science and Telecommunications Engineering of Jiangsu University. His current research interests lie in the areas of pattern recognition, computational intelligence and computer vision.
    Ya-Bi Chen received the B.S. degree from the School of Computer Science and Telecommunication Engineering of Jiangsu University in 2007. He is currently a postgraduate student in the School of Computer Science and Telecommunication Engineering of Jiangsu University. His research interests include pattern recognition and image processing.
    Chuan-Jun Wen received the M.S. degree in applied mathematics from Chongqing University, China, in 2002. At present, he is a Ph.D. candidate in the School of Computer Science and Telecommunication Engineering of Jiangsu University. His research interests include pattern recognition and image processing.
  • Supported by:

    This work is supported by the National Natural Science Foundation of China under Grant No. 60673190.

To overcome the disadvantage of classical recognition model that cannot perform well enough when there are some noises or lost frames in expression image sequences, a novel model called fuzzy buried Markov model (FBMM) is presented in this paper. FBMM relaxes conditional independence assumptions for classical hidden Markov model (HMM) by adding the specific cross-observation dependencies between observation elements. Compared with buried Markov model (BMM), FBMM utilizes cloud distribution to replace probability distribution to describe state transition and observation symbol generation and adopts maximum mutual information (MMI) method to replace maximum likelihood (ML) method to estimate parameters. Theoretical justifications and experimental results verify higher recognition rate and stronger robustness of facial expression recognition for image sequences based on FBMM than those of HMM and BMM.


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