Improving Cross-View Gait Recognition With Generative Adversarial Networks

The performance of gait recognition can be obviously affected by view angle variation. In this paper, we present a new method which uses a view transformation generative adversarial networks (GAN) to improve performance of dealing with cross-view gait recognition problem. Our proposed method firstly trains a convolutional neural network (CNN) using gait energy image (GEI) for recognition. Then, a GAN model is taken as a generator to transform gait images with variety view angle to unique side view images. In order to preserve the identification information of generated images, the generated images are input into the fixed pre-trained CNN and recognition loss is used to update generator. Finally, we combine the distance matrix of original and generated image and get final recognition results. We conduct experiments to demonstrate the improvement of adding GAN branch on three popular gait dataset. Experimental results show that our method can achieve state-of-the-art performance.

Gait recognition, generative adversarial networks,cross view

Rui Zhang, Dong Yin, Zhipeng Zhou, Zhi Cao, Fanjun Meng, Bin Hu, Improving Cross-View Gait Recognition With Generative Adversarial Networks. 2019 Scientific Conference on Network, Power Systems and Computing (NPSC 2019), 2019: 43-47. DOI: https://doi.org/10.33969/EECS.V3.011.