  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.
