Yuxiang Shan, Gang Yu, Yanghua Gao, Journal of Information Processing Systems Vol. 20, No. 6, pp. 801-811, Dec. 2024  

https://doi.org/10.3745/JIPS.02.0222
Keywords: Block Collaborative Gait Representation, Dilated Convolution, gait recognition, Residual Mechanism
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Abstract

Human identification based on gait analysis is a promising biometric technology that can recognize different individuals by their walking patterns. This study primarily addresses the challenges of gait representation and partial occlusion. Firstly, considering the multi-scale and multi-perspective aspects of gait in practical application scenarios, a novel block collaborative gait representation method is proposed based upon local structures, aiming to enhance the accuracy of identity recognition by integrating information from multiple scales and perspectives. Then, we propose a new gait recognition network that integrates dilated convolutions and the residual mechanism (DCRM). The DCRM network adds dilated convolutional blocks to the residual branch to expand the receptive field without losing resolution, thereby reducing the negative impact of local occlusion on recognition accuracy. Experimental results on two public datasets demonstrated that the proposed approach shows clear advantages over existing gait analysis methods.

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