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New semantic descriptor construction for facial expression recognition based on axiomatic fuzzy set

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Abstract

In this paper, we propose a new semantic descriptor based on axiomatic fuzzy set (AFS) to describe facial expressions. The new descriptor has two advantages: The first one is that it does not depend on priori-knowledge, when one uses it to construct semantic concepts. According to the distribution of feature data, one can quickly establish semantic concepts using the fuzzy membership degree. The second one is that the descriptor can describe complex features by implementing operation on semantic concepts. The developed descriptor can provide variations and relations of expression features. Finally, we implement our method on FEI and CK+ database, and make semantic interpretations for various expressions. Meanwhile, the performance is evaluated with the state-of-the-art methods such as C4.5, Bayes, Decision Table, Cart and Reduced error pruning tree.

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Acknowledgements

This work is supported by Natural Science Foundation of China (No.61370146, 61672132) and Liaoning Science & Technology of Liaoning Province of China (No.2013405003).

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Authors and Affiliations

  1. Institute of Systems Science, Northeastern University, Shenyang, Liaoning, China

    Zedong Li, Qingling Zhang & Cunrui Wang

  2. Dalian Key Laboratory of Digital Technology for National Culture, Dalian Minzu University, Dalian, Liaoning, China

    Xiaodong Duan, Cunrui Wang & Yu Shi

Authors
  1. Zedong Li
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  2. Qingling Zhang
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  3. Xiaodong Duan
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  4. Cunrui Wang
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  5. Yu Shi
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Correspondence to Xiaodong Duan.

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Li, Z., Zhang, Q., Duan, X. et al. New semantic descriptor construction for facial expression recognition based on axiomatic fuzzy set. Multimed Tools Appl 77, 11775–11805 (2018). https://doi.org/10.1007/s11042-017-4818-3

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  • DOI: https://doi.org/10.1007/s11042-017-4818-3

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