Gait recognition under multiple views is an important computer vision and pattern recognition task. In the emerging convolutional neural network based approaches, the information of view angle is ignored to some extent. Instead of direct view estimation and training view-specific recognition models, we propose a compatible framework that can embed view information into existing architectures of gait recognition. The embedding is simply achieved by a selective projection layer. Experimental results on two large public datasets show that the proposed framework is very effective.
Gait is an important biometric that can recognize people at a distance. Recently, Disentangled Representation Learning (DRL) has been introduced for distinguishing identityirrelevant covariate features from identity features for better recognition performance. However, such a simple gait energy image (GEI) pairing operation inevitably brings in over-disentanglement effects that degrade the performance. To address this issue, we proposed a covariate feature control gate module that compensates for the discriminative feature loss by using additional semantic labels. Furthermore, a shared attention module, which allows the identity and covariate part to pay attention to different spatial regions, is also proposed for better spatial disentanglement. Experimental results show that our method outperforms the stateof-the-art and well-explain the mechanism of how the improvement is achieved. The code is available at https: //github.com/ctrasd/GA-ICDNet.
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