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End-to-end Model-based Gait Recognition using Synchronized Multi-view Pose Constraint

We propose an end-to-end model-based cross-view gait recognition which employs pose sequences and shapes extracted by human model fitting. Specifically, we consider a problem setting where gait sequences from single different views are given as a pair to match in a test phase, while asynchronous multi-view gait sequences are given for each subject in a training phase. This work exploits multi-view constraint in the training phase to extract more consistent pose sequences from any views in the test phase, unlike the existing methods do not consider them. For this purpose, given asynchronous multi-view gait sequences, we introduce a phase synchronization step in the training phase so that we can impose pose consistency at each synchronized phase in a temporally up-sampled phase domain. We then train our network by minimizing a loss function based on the synchronized multi-view pose constraint as well as shape consistency, temporal pose smoothness, recognition accuracy, etc in an end-to-end manner. We also introduce the synchronization step in a test phase to reduce intra-subject variations caused by asynchronous pose features. Experimental results on the OU-MVLP and CASIA-B datasets show that the proposed method achieves the state-of-the-art performance for both gait identification and verification scenarios, especially a great improvement in terms of the pose representations.