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Feature fusion of multi-granularity and multi-scale for facial expression recognition

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Abstract

Although great progress has been made in facial expression recognition, it still faces challenges such as occlusion and pose changes in real-world scenario. To address this issue, we propose a simple yet effective multi-granularity and multi-scale feature fusion network (MM-Net) to achieve robust expression recognition without either manually extracting local patches or designing complex sub-networks. Specifically, we use a puzzle generator to divide the image into local regions of different granularity, which are then randomly shuffled and reorganized to form a new input image. By feeding the facial puzzles in order from fine-grained to coarse-grained, the network progressively mines the local fine-grained information, the coarse-grained information, and the global information. Besides, considering the subtle inter-class variation characteristic of different expressions, we use the multi-scale feature fusion strategy in the shallow feature extraction module to obtain global features with detailed information for capturing the subtle differences in facial expression images. Extensive experimental results on three in-the-wild FER benchmarks demonstrate the superiority of the proposed MM-Net compared to state-of-the-art methods.

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Data Availability

The RAF-DB datasets analyzed during the current study are available in http://www.whdeng.cn/raf/model1.html. The FERPlus datasets analyzed during the current study are available in https://github.com/Microsoft/FERPlus. The AffectNet datasets analyzed during the current study are available in http://mohammadmahoor.com/affectnet/. The FED-RO datasets analyzed during the current study are available in https://doi.org/10.1109/TIP.2018.2886767.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos.62106054), the Science and Technology Project of Guangxi (Grant 2018GXNSFAA281351) and the Research Projects of Guangxi Normal University (Natural Sciences) (2021JC012).

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  1. School of Electronic and Information Engineering, Guangxi Normal University, Guilin, 541004, China

    Haiying Xia, Lidan Lu & Shuxiang Song

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Xia, H., Lu, L. & Song, S. Feature fusion of multi-granularity and multi-scale for facial expression recognition. Vis Comput 40, 2035–2047 (2024). https://doi.org/10.1007/s00371-023-02900-3

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