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A two-branch hand gesture recognition approach combining atrous convolution and attention mechanism

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Abstract

Hand gesture recognition is an important research field in computer vision. To effectively solve the problem of low hand gesture recognition accuracy, we propose two modules by using atrous convolution in this paper. One is Multi-Scale Fusion (MSF) module. The other is Light-Weight Multi-Scale (LWMS) module. The MSF module can be used for extracting multi-scale features at different receptive fields. The LWMS module can be considered as a kind of enhanced and expanded convolutional operation. Based on the two modules, a Hand Gesture Recognition Approach called HGRA is designed. HGRA is a hand gesture recognition approach which is based on an end-to-end CNN-based framework with two branches. One branch uses the U-Net combined with Multi-Scale Attention module to perform hand gesture segmentation in order to separate hand gestures from complex backgrounds. Then the segmentation result is used for extracting shape features. The other branch extracts visual features, such as appearance and color. The shape and the visual features obtained by the two branches are integrated to perform hand gesture recognition. Experimental results on the OUHANDS and HGR1 gesture datasets show that the proposed method has competitive performance both in hand gesture segmentation and recognition.

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Funding

This work was supported partly by the National Natural Science Foundation of China[grant numbers 61379065]; the Natural Science Foundation of Hebei province in China [grant numbers F2019203285].

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Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China

    Shi Wang, Shihui Zhang & Xiaowei Zhang

  2. Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, 066004, China

    Shihui Zhang & Xiaowei Zhang

  3. Hebei Normal University of Science & Technology, Qinhuangdao, 066004, China

    Shi Wang & Qingjia Geng

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  1. Shi Wang
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  2. Shihui Zhang
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Correspondence to Shi Wang or Shihui Zhang.

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Wang, S., Zhang, S., Zhang, X. et al. A two-branch hand gesture recognition approach combining atrous convolution and attention mechanism. Vis Comput 39, 4487–4500 (2023). https://doi.org/10.1007/s00371-022-02602-2

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