GaitContour: Efficient Gait Recognition Based on a Contour-Pose Representation

Gait recognition holds the promise to robustly identify subjects based on walking patterns instead of appearance information. In recent years, this field has been dominated by learning methods based on two input formats: silhouette images and sparse keypoints. Compared to image-based approaches, keypoint-based methods can achieve significantly higher efficiency due to their sparsity. However, sparsity also results in information loss, thereby reducing performance. In this work, we propose a novel, keypoint-based Contour-Pose representation, which compactly encodes both body shape and parts information. We further propose a local-to-global architecture, called GaitContour, to leverage this novel representation and efficiently compute subject embedding in two stages. The first stage consists of a local transformer that extracts features from five different body regions. The second stage then aggregates the regional features to estimate a global human gait representation. Such a design significantly reduces the complexity of the attention operation and improves both efficiency and performance. Through large scale experiments, Gait-Contour is shown to perform significantly better than previous keypoint-based methods. Furthermore, the ContourPose representation also achieves new SoTA performances on fusion-based gait recognition methods.