A new method for gait recognition with 2D pose estimation

Han Yan; Yan Piao; Xiunan Li

doi:10.1117/12.2639153

15 July 2022 A new method for gait recognition with 2D pose estimation

Han Yan, Yan Piao, Xiunan Li

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 122580U (2022) https://doi.org/10.1117/12.2639153
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

This paper proposes a new model-based gait recognition method. Different from other methods using 3D (3-dimensional) keypoint information and skeleton information, we directly stack the 2D (2-dimensional) keypoint heatmaps in the gait sequence in the time dimension, and input it into the network structure based on 3D-CNN (3-dimensional-convolutional neural network). Then, through the gait analysis on the two dimensions of time and space, the effective gait features are finally obtained. Compared with other model-based methods, this method is more clear, concise and elegant in the process of feature extraction. The test of CASIA-B dataset shows that in the model-based gait recognition method, we have competitive performance.

Citation Download Citation

Han Yan, Yan Piao, and Xiunan Li "A new method for gait recognition with 2D pose estimation", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 122580U (15 July 2022); https://doi.org/10.1117/12.2639153

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KEYWORDS

Gait analysis

3D modeling

Model-based design

Feature extraction

Data modeling

Performance modeling

Convolution

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