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Rotation-reversal invariant HOG cascade for facial expression recognition

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Abstract

This paper presents a novel classification framework derived from AdaBoost to classify facial expressions. The proposed framework adopts rotation-reversal invariant HOG as features. The framework is implemented by configuring the area under receiver operating characteristic curve of the weak classifier with HOG, which is a discriminative classification framework. The proposed classification framework is evaluated with three very popular and representative public databases: CK+, MMI, and AFEW. The results showed that the proposed classification framework outperforms the state-of-the-art methods.

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Authors and Affiliations

  1. RIEB, Kobe University, Kobe, 657-8501, Japan

    Jinhui Chen

  2. The Organization of Advanced Science and Technology, Kobe University, Kobe, 657-8501, Japan

    Tetsuya Takiguchi & Yasuo Ariki

Authors
  1. Jinhui Chen
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  2. Tetsuya Takiguchi
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  3. Yasuo Ariki
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Correspondence to Jinhui Chen.

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Chen, J., Takiguchi, T. & Ariki, Y. Rotation-reversal invariant HOG cascade for facial expression recognition. SIViP 11, 1485–1492 (2017). https://doi.org/10.1007/s11760-017-1111-x

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  • DOI: https://doi.org/10.1007/s11760-017-1111-x

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