End-to-End Model-Based Gait Recognition

Abstract

Most existing gait recognition approaches adopt a two-step procedure: a preprocessing step to extract silhouettes or skeletons followed by recognition. In this paper, we propose an end-to-end model-based gait recognition method. Specifically, we employ a skinned multi-person linear (SMPL) model for human modeling, and estimate its parameters using a pre-trained human mesh recovery (HMR) network. As the pre-trained HMR is not recognition-oriented, we fine-tune it in an end-to-end gait recognition framework. To cope with differences between gait datasets and those used for pre-training the HMR, we introduce a reconstruction loss between the silhouette masks in the gait datasets and the rendered silhouettes from the estimated SMPL model produced by a differentiable renderer. This enables us to adapt the HMR to the gait dataset without supervision using the ground-truth joint locations. Experimental results with the OU-MVLP and CASIA-B datasets demonstrate the state-of-the-art performance of the proposed method for both gait identification and verification scenarios, a direct consequence of the explicitly disentangled pose and shape features produced by the proposed end-to-end model-based framework.