DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition

• URL: http://arxiv.org/abs/2303.14953v1
• Date: Mon, 27 Mar 2023 07:36:47 GMT
• Title: DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition
• Authors: Ming Wang, Xianda Guo, Beibei Lin, Tian Yang, Zheng Zhu, Lincheng Li, Shunli Zhang and Xin Yu
• Abstract summary: Gait recognition is a biometric technology that recognizes the identity of humans through their walking patterns. We propose a novel and high-performance framework named DyGait to focus on the extraction of dynamic features. Our network achieves an average Rank-1 accuracy of 71.4% on the GREW dataset, 66.3% on the Gait3D dataset, 98.4% on the CASIA-B dataset and 98.3% on the OU-M dataset.
• Score: 35.642868929840034
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Gait recognition is a biometric technology that recognizes the identity of humans through their walking patterns. Compared with other biometric technologies, gait recognition is more difficult to disguise and can be applied to the condition of long-distance without the cooperation of subjects. Thus, it has unique potential and wide application for crime prevention and social security. At present, most gait recognition methods directly extract features from the video frames to establish representations. However, these architectures learn representations from different features equally but do not pay enough attention to dynamic features, which refers to a representation of dynamic parts of silhouettes over time (e.g. legs). Since dynamic parts of the human body are more informative than other parts (e.g. bags) during walking, in this paper, we propose a novel and high-performance framework named DyGait. This is the first framework on gait recognition that is designed to focus on the extraction of dynamic features. Specifically, to take full advantage of the dynamic information, we propose a Dynamic Augmentation Module (DAM), which can automatically establish spatial-temporal feature representations of the dynamic parts of the human body. The experimental results show that our DyGait network outperforms other state-of-the-art gait recognition methods. It achieves an average Rank-1 accuracy of 71.4% on the GREW dataset, 66.3% on the Gait3D dataset, 98.4% on the CASIA-B dataset and 98.3% on the OU-MVLP dataset.

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