A survey on gait recognition against occlusion: taxonomy, dataset and methodology.

Traditional biometric techniques often require direct subject participation, limiting application in various situations. In contrast, gait recognition allows for human identification via computer analysis of walking patterns without subject cooperation. However, occlusion remains a key challenge limiting real-world application. Recent surveys have evaluated advances in gait recognition, but only few have focused specifically on addressing occlusion conditions. In this article, we introduces a taxonomy that systematically classifies real-world occlusion, datasets, and methodologies in the field of occluded gait recognition. By employing this proposed taxonomy as a guide, we conducted an extensive survey encompassing datasets featuring occlusion and explored various methods employed to conquer challenges in occluded gait recognition. Additionally, we provide a list of future research directions, which can serve as a stepping stone for researchers dedicated to advancing the application of gait recognition in real-world scenarios.