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A convolution-transformer dual branch network for head-pose and occlusion facial expression recognition

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Abstract

Facial expression recognition (FER) has attracted much more attention due to its broad range of applications. Occlusions and head-pose variations are two major obstacles for automatic FER. In this paper, we propose a convolution-transformer dual branch network (CT-DBN) that takes advantage of local and global facial information to tackle the real-word occlusions and head-pose variant robust FER. The CT-DBN contains two branches. Taking into account local modeling ability of CNN, the first branch utilizes CNN to capture local edge information. Inspired by transformers’ successful application in natural language processing, we employ transformer to the second branch to be responsible for obtaining better global representation. Then, a local–global feature fusion module is proposed to adaptively integrate both features to hybrid features and model the relationship between them. With the help of feature fusion module, our network not only integrates local and global features in an adaptive weighting manner but can also learn the corresponding distinguishable features autonomously. Experimental results under inner-database and cross-database evaluation on four leading facial expression databases illustrate that our proposed CT-DBN outperforms other state-of-the-art methods and achieves robust performance under in-the-wild condition.

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Authors and Affiliations

  1. Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China

    Xingcan Liang, Jinfu Liu & Zhipeng Liu

  2. University of Science and Technology of China, Hefei, 230026, China

    Xingcan Liang, Jinfu Liu & Zhipeng Liu

  3. School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213164, China

    Wenxiang Zhang

  4. School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, 230009, China

    Yan Zhang

  5. College of Mechanical and Electrical Engineering, Hohai University, Changzhou, 213022, China

    Linsen Xu

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  1. Xingcan Liang

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  2. Linsen Xu

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  3. Wenxiang Zhang

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This work was partially supported by National Key R&D Program of China (Grant No. 2017YFB1303200), Jiangsu Special Project for Frontier Leading Base Technology (Grant No. BK20192004), Key Support Project of Dean Fund of Hefei Institutes of Physical Science, CAS (Grant No. YZJJZX202017), and Strategic High-tech Innovation Fund of Chinese Academy of Sciences (Grant No. GQRC-19-15).

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Liang, X., Xu, L., Zhang, W.et al. A convolution-transformer dual branch network for head-pose and occlusion facial expression recognition.Vis Comput39, 2277–2290 (2023). https://doi.org/10.1007/s00371-022-02413-5

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