Multiview Gait Recognition Based on Slack Allocation Generation Adversarial Network

Abstract

Through the in-depth study of biological characteristics, the gait information of each person is found to be unique. Therefore, gait characteristics can be used to identify a person’s identity information. However, pedestrian gait information is often affected by various interference factors such as viewing angle, dress, and carrying objects. To solve this problem, the idea of perspective transformation is proposed to transform gait images from different perspectives and different states to gait images under standard conditions on one side. Generative adversarial network (GAN) is adopted for perspective transformation. Moreover, in order to solve the difficulty and instability to converge characteristics of GAN in the training process, we introduced a slack module to adjust the shortcomings of the GAN. Experiments show that under normal walking conditions, our method achieves an average accuracy of 98.32% on the CASIA-B gait dataset.