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OpenFE: feature-extended OpenMax for open set facial expression recognition

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Abstract

Open-set methods are crucial for rejecting unknown facial expressions in real-world scenarios. Traditional open-set methods primarily rely on a single feature vector for constructing the centers of known facial expression categories, which limits their ability to discriminate unknown categories. To address this problem, we propose the OpenFE method. This method introduces an attention mechanism that focuses on critical regions to improve the quality of feature vectors. Simultaneously, reconstruction methods are employed to extract low-dimensional potential features from images. By enriching the feature representation of known categories, the OpenFE method significantly amplifies the differentiation between unknown and known facial categories. Extensive experimental validation demonstrates the exceptional performance of the OpenFE method in expression open set classification, confirming its robustness.

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Author notes

  1. Jie Shao, Jiacheng Wu, and Wenzhong Shen contributed equally to this work.

Authors and Affiliations

  1. Department of Electronic and Information Engineering, Shanghai University of Electric Power, 1851 Hucheng Ring Road, Shanghai, 200120, China

    Jie Shao, Zicheng Song, Jiacheng Wu & Wenzhong Shen

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  1. Jie Shao
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  2. Zicheng Song
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  3. Jiacheng Wu
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Correspondence to Zicheng Song.

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Shao, J., Song, Z., Wu, J. et al. OpenFE: feature-extended OpenMax for open set facial expression recognition. SIViP 18, 1355–1364 (2024). https://doi.org/10.1007/s11760-023-02843-1

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