Abstract
Recently, emotion recognition through gait, which is more difficult to imitate than other biological characteristics, has aroused extensive attention. Although some deep-learning studies have been conducted in this field, there are still two challenges. First, it is hard to extract the representational features of the gait from video effectively. Second, the input of body joints sequences has noise introduced during dataset collection and feature production. In this work, we propose a global link, which extends the existing skeleton graph (the natural link) to capture the overall state of gait based on spatial-temporal convolution. In addition, we use soft thresholding to reduce noise. The thresholds are learned automatically by a block called shrinkage block. Combined with the global link and shrinkage block, we further propose the global graph convolution shrinkage network (G-GCSN) to capture the emotion-related features. We validate the effectiveness of the proposed method on a public dataset (i.e., Emotion-Gait dataset). The proposed G-GCSN achieves improvements compared with state-of-the-art methods.
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Acknowledgements
This work was supported in part by Major Scientific Research Project of Zhejiang Lab under the Grant No. 2018DG0ZX01, and in part by the Grant in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant No. 20K21821.
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Zhuang, Y., Lin, L., Tong, R., Liu, J., Iwamoto, Y., Chen, YW. (2021). G-GCSN: Global Graph Convolution Shrinkage Network for Emotion Perception from Gait. In: Sato, I., Han, B. (eds) Computer Vision – ACCV 2020 Workshops. ACCV 2020. Lecture Notes in Computer Science(), vol 12628. Springer, Cham. https://doi.org/10.1007/978-3-030-69756-3_4
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