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Decomposing Identity and View for Cross-View Gait Recognition

One of many challenges in cross-view gait recognition is that a pedestrian's gaits have huge visual differences under different views. A promising solution to this challenge is to extract fundamental discriminative view-invariant gait features, so that gait recognition systems can be robust to viewpoint varying. In order to extract discriminative gait features, this paper proposes a method to decompose identity and view for cross-view gait recognition. Our method employs a newly designed auto-encoder to detach the identity features from the view features. We train our model with the classification and metric learning to obtain discriminative identity feature. Also, both the view regression and identity ambiguity are applied to ensure that the view features only contain view information. Finally, a dissimilarity loss is proposed to increase the distribution discrepancy between identity feature and view feature, so as to further refine the identity feature. The experimental results on the CASIA-B and OU-ISIR databases show that the proposed method performs well in cross-view gait recognition.