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

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Abstract

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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Authors and Affiliations

  1. School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, 212013, China

    Yong-Zhao Zhan, Ke-Yang Cheng, Ya-Bi Chen & Chuan-Jun Wen

Authors
  1. Yong-Zhao Zhan
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  2. Ke-Yang Cheng
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  3. Ya-Bi Chen
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  4. Chuan-Jun Wen
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Correspondence to Yong-Zhao Zhan.

Additional information

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

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Zhan, YZ., Cheng, KY., Chen, YB. et al. A New Classifier for Facial Expression Recognition: Fuzzy Buried Markov Model. J. Comput. Sci. Technol. 25, 641–650 (2010). https://doi.org/10.1007/s11390-010-9353-x

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  • DOI: https://doi.org/10.1007/s11390-010-9353-x

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