  In this paper, we develop a novel convolutional neural network based approach
to extract and aggregate useful information from gait silhouette sequence
images instead of simply representing the gait process by averaging silhouette
images. The network takes a pair of arbitrary length sequence images as inputs
and extracts features for each silhouette independently. Then a feature map
pooling strategy is adopted to aggregate sequence features. Subsequently, a
network which is similar to Siamese network is designed to perform recognition.
The proposed network is simple and easy to implement and can be trained in an
end-to-end manner. Cross-view gait recognition experiments are conducted on
OU-ISIR large population dataset. The results demonstrate that our network can
extract and aggregate features from silhouette sequence effectively. It also
achieves significant equal error rates and comparable identification rates when
compared with the state of the art.
