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Coupled Patch Alignment for Matching Cross-View Gaits
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DOI：10.1109/TIP.2019.2894362.png)
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摘要
En 中文
Gait recognition has attracted growing attention in recent years, as the gait of humans has a strong discriminative ability even under low resolution at a distance. Unfortunately, the performance of gait recognition can be largely affected by view change. To address this problem, we propose a coupled patch alignment (CPA) algorithm that effectively matches a pair of gaits across different views. To realize CPA, we first build a certain amount of patches, and each of them is made up of a sample as well as its intra-class and inter-class nearest neighbors. Then, we design an objective function for each patch to balance the cross-view intra-class compactness and the cross-view inter-class separability. Finally, all the local-independent patches are combined to render a unified objective function. Theoretically, we show that the proposed CPA has a close relationship with canonical correlation analysis. Algorithmically, we extend CPA to multi-dimensional patch alignment that can handle an arbitrary number of views. Comprehencise experiments on CASIA(B), USF, and OU-ISIR gait databases firmly demonstrate the effectiveness of our methods over other existing popular methods in terms of cross-view gait recognition.
Keyword:
Coupled patch alignment
gait recognition
cross-view gait
multi-dimensional patch alignment
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13.7
论文数:
1.0W
被引数:
8.4W
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