Occluded Gait Recognition via Silhouette Registration Guided by Automated Occlusion Degree Estimation

Chi Xu, Shogo Tsuji, Yasushi Makihara, Xiang Li, Yasushi Yagi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023, pp. 3199-3209

Gait recognition tasks often face significant difficulties caused by partial occlusions of the human body. To address this challenge, we propose a silhouette registration method based on flexible estimation of the spatial scale associated with the occluding elements. Existing appearance-based methods require prior knowledge about the spatial scale of the human body in relation to the input image, or a bounding box that includes the actual full body. In our method, the region corresponding to the silhouette of the body is estimated directly from visible body parts within the image. This estimate is then used to normalize and register the human body by adapting it to the scale of the occlusions. To reduce the occlusion difference between elements of a matching pair, which may lead to substantial intra-subject variation when the difference is large, we use a pairwise mask to extract common visible regions for subsequent feature learning and matching. Experiments on the synthetic occluded OU-MVLP dataset demonstrate the effectiveness of the proposed method, which successfully improves recognition performance when matching pairs present occlusion differences. We discuss specific characteristics of the proposed silhouette registration and pairwise masking methods with the aid of detailed quantitative and qualitative evaluations, in the hope of providing useful insights for future research on this topic.