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Two-stream inter-class variation enhancement network for facial expression recognition

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Abstract

Automatic facial expression recognition plays a crucial role in computer vision, and pattern recognition. However, most existing deep learning-based facial expression classifiers usually obtain high average accuracy but have poor recognition accuracy for difficult expressions, like fear and disgust. In this paper, we propose a novel end-to-end architecture termed two-stream inter-class variation enhancement network, which learns the high-level semantic features and subtle inter-class variations in a joint fashion. More precisely, the global feature extraction network is used to extract spatial-channel semantic features, and the variations between different expressions are modeled by a distinction-reinforced network. The outputs of these two streams are weighted integrated in the expression classification network. In addition, a class balanced-weighted cross-entropy loss is designed to further improve feature discrimination. Experiment results indicate that the proposed network can significantly improve the recognition of difficult expressions and achieve a satisfactory average recognition accuracy of 73.67% on FER2013, 86.17% on RAFDB, 98.19% on CK+, and 98.85% on Oulu-CASIA, which outperforms the other state-of-the-art methods.

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Acknowledgements

This work was supported by the Special Project on Basic Research of Frontier Leading Technology of Jiangsu Province of China (Grant No. BK20192004C) and the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20181269).

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  1. School of Automation, Southeast University, Nanjing, 210096, Jiangsu, China

    Qian Jiang, Ziyu Zhang, Feipeng Da & Shaoyan Gai

  2. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, 210096, Jiangsu, China

    Qian Jiang, Ziyu Zhang, Feipeng Da & Shaoyan Gai

  3. Shenzhen Research Institute, Southeast University, Shenzhen, 518063, Guangdong, China

    Feipeng Da

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Jiang, Q., Zhang, Z., Da, F. et al. Two-stream inter-class variation enhancement network for facial expression recognition. Vis Comput 39, 5209–5227 (2023). https://doi.org/10.1007/s00371-022-02655-3

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