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Three-dimensional convolutional neural networks applied to video sensor-based gait recognition
Author(s): Xianfu Zhang; Nuo Xu; Xurui Zhang; Shouqian Sun; Yuping Hu
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Paper Abstract

In this paper, we propose a novel gait representation based on 3D-CNN, i.e., learning spatio-temporal multi-scale gait identity features (GaitID) using the 3-dimensional convolutional networks. Our contributions include: 1) explore different numbers of input frames for 3D-CNN model, 2) evaluate different features and gait representations in 3D-CNN, and 3) improve the net structure to learn multi-scale gait features with low dimensions. Nearest neighbor (NN) classifier was applied to identify the gait. When compared with other existing methods, the results reported on the CASIA-B dataset demonstrated that the proposed method not only achieved a competitive performance, but also still retained the discriminative power in a very low dimension (128-D), even with a simpler classifier.

Paper Details

Date Published: 9 August 2018
PDF: 9 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061V (9 August 2018); doi: 10.1117/12.2502828
Show Author Affiliations
Xianfu Zhang, Zhejiang Univ. (China)
Wuzhou Univ. (China)
Nuo Xu, China Academy of Launch Vehicle Technology (China)
Xurui Zhang, Zhejiang Univ. (China)
Shouqian Sun, Zhejiang Univ. (China)
Yuping Hu, Wuzhou Univ. (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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