Improved Gait recognition based on specialized deep convolutional neural networks

Gait recognition is a biometric technique that is used in order to determine the identity of humans based on the style and the manner of their walk. Yet, Gait recognition performance is often degraded by some covariate factors such as a viewing angle variation, clothing and carrying condition changes, and a low-image resolution. In general, current tactics to object recognition highly depend on the use of machine learning techniques. Therefore, a deep convolutional neural network (CNN) is one of the most advanced machine learning techniques that has the ability to approximate complex non-linear functions from high-dimensional input data in a hierarchical process. In this paper, we develop a specialized deep CNN architecture, which consists of multilayers of convolutional and subsampling layers. The proposed technique is less sensitive to several cases of the common variations and occlusions that affect and degrade gait recognition performance. We avoided the use of the typical subspace learning methods, along with its shortcomings, that are widely used in gait recognition. When applied the proposed deep CNN model to CASIA-B large gait database, the experimental results show that the deep CNN model developed in this paper outperforms the other state of art gait recognition techniques in several cases.