Gait Recognition using Deep Residual Networks and Conditional Generative Adversarial Networks

In biometric authentication, the subject of distinguishing people by their gait is considered very important. However, it suffers from several challenges, including changing the angle of walking, wearing a coat, as well as wearing high shoes. The development of artificial intelligence, especially the subject of deep learning, has made a breakthrough in this field. In this research, a new technique was proposed that depends on the use of the conditional generative adversarial network in addition to the ResNet network in order to generate and classify images. The new method relies on the side view angle because it generates various body characteristics. The framework can be divided into three parts: the first part is the process of extracting silhouettes, calculating the gait cycle, and then calculating gait energy images. The second part is the process of generating images through conditional generative adversarial networks and discriminators models. The third part is the process of classifying images using the ResNet network. To evaluate our framework, experiments are performed on a public gait recognition dataset called CASIA database. Results demonstrate that the proposed three-stage framework works better than cutting-edge methods, particularly in carrying-bag and wearing-coat sequences.