Gait Energy Image Restoration Using Generative Adversarial Networks

Abstract

Gait is a biometric property that can be used for human identification in video surveillance. Basically, different gait features require motion of a person walking over one complete gait cycle. For example, in Gait Energy Image (GEI), average of silhouette images over one complete gait cycle is computed. However, in reality, there might be a partial gait cycle data available due to occlusion. In this paper, we propose a Generative Adversarial Network (GAN) in order to address the problem of gait recognition from incomplete gait cycle. Precisely, the network is able to reconstruct complete GEIs from incomplete GEIs. The proposed architecture is composed of (i) a generator which is an auto-encoder network to construct complete GEIs out of incomplete GEIs and (ii) two discriminators, one of which discriminates whether a given image is a full GEI while the other discriminates whether two GEIs belong to the same subject. We evaluate our approach on the OULP large gait dataset confirming that the proposed architecture successfully reconstructs complete GEIs from even extreme incomplete gait cycles.