Multi-stream part-fused graph convolutional networks for skeleton-based gait recognition
Gait recognition, a task of identifying people through their walking pattern, has attracted more and more researchers' attention. At present, most skeleton-based gait recognition approaches extract gait features from merely joint coordinates. However, the information, e.g. bone and motion, is e...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2026294 |
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| Summary: | Gait recognition, a task of identifying people through their walking pattern, has attracted more and more researchers' attention. At present, most skeleton-based gait recognition approaches extract gait features from merely joint coordinates. However, the information, e.g. bone and motion, is equally instructive and discriminative for gait recognition. Thus, this paper proposes a novel multi-stream part-fused graph convolutional network, MS-Gait, to fuse part-level information and capture multi-order features from skeleton data. To be specific, we integrate a channel attention learning mechanism into the graph convolutional networks (GCN) to improve the representational power. In addition, part-level information is merged by capturing features from the skeleton graph and its subgraphs concurrently. Finally, a multi-stream strategy is proposed to model joint, bone, and motion dynamics simultaneously, which is proven to effectively improve the recognition accuracy. On the popular CASIA-B dataset, extensive experiments demonstrate that our method can achieve state-of-the-art performance and is robust to confounding variations. |
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| ISSN: | 0954-0091 1360-0494 |