Gait recognition using multichannel convolution neural networks

Human gait recognition has a wide range of applications in multiple fields, such as video surveillance, digital security, and forensics. In this paper, we investigate the challenging problem of cross-view gait recognition and propose a novel gait recognition scheme by utilizing the strong expression of convolution neural networks (CNN). First, instead of using gait energy images in traditional gait recognition, we will design a new gait feature representation, trituple gait silhouettes, constructed by using consecutive gait silhouette pictures. Second, we will construct a multichannel CNN network to tackle a set of sequential images in parallel. Each of the image datasets is treated as one input channel, and a different convolutional kernel is used. Finally, the proposed approach is evaluated extensively based on the CASIA gait dataset A/B for cross-view gait recognition, and further on the OU-ISIR large population gait dataset to verify its generalization capability with large-scale data. To the best of our knowledge, this is the first time that this gait recognition scheme is presented. All our experimental results show that the proposed method obtains better performance when compared to those existing methods.