Improving Gait Recognition Through Occlusion Detection and Silhouette Sequence Reconstruction

Abstract

Gait recognition is an advanced biometric technology that can be used to identify individuals based on their walking patterns, even from low-spatial-resolution image sequences from security surveillance camera footage. Traditional gait recognition approaches rely on complete body information and often overlook the challenge of occlusion. In real-world scenarios, various body parts may be occluded by physical obstacles such as buildings, walls, fences, vehicles, trees, or even other individuals in crowded areas. This occlusion results in a significant portion of the human body being unobserved, causing conventional gait recognition approaches to fail to identify the person. To address this challenge, we have developed a novel framework for gait recognition in the presence of occlusion, incorporating occlusion detection and reconstruction (ODR) and feature extraction for gait recognition (FEGR) modules. The ODR module identifies the occlusion type and reconstructs the occluded portions of the human body in a silhouette sequence using three-dimensional (3D) generative adversarial networks, whereas the FEGR module extracts partwise global and local features using 3D convolutional neural networks (CNNs) and full body features on a frame-by-frame basis using two-dimensional CNNs. We validated our framework using the CASIA-B and OU-MVLP datasets with artificially added occlusions and found that it showed superior performance, with average rank-1 accuracies of 96.4%, 87.8%, and 69.2% for normal, carried object, and clothing variations on CASIA-B and 58.9% on OU-MVLP, as well as 100.0% occlusion detection accuracy. These results demonstrate the ability of our proposed framework to maintain superior gait recognition performance despite the presence of occlusions.