DeformGait: Gait Recognition under Posture Changes using Deformation Patterns between Gait Feature Pairs

Abstract

In this paper, we propose a unified convolutional neural network (CNN) framework for robust gait recognition against posture changes (e.g., those induced by walking speed changes). In order to mitigate the posture changes, we first register an input matching pair of gait features with different postures by a deformable registration network, which estimates a deformation field to transform the input pair both into their intermediate posture. The pair of the registered features is then fed into a recognition network. Furthermore, ways of the deformation (i.e., deformation patterns) can differ between the same subject pairs (e.g., only posture deformation) and different subject pairs (e.g., not only posture deformation but also body shape deformation), which implies the deformation pattern can be another cue to distinguish the same subject pairs from the different subject pairs. We therefore introduce another recognition network whose input is the deformation pattern. Finally, the deformable registration network, and the two recognition networks for the registered features and the deformation patterns, constitute the whole framework, named DeformGait, and they are trained in an end-to-end manner by minimizing a loss function which is appropriately designed for each of verification and identification scenario. Experiments on the publicly available dataset containing the largest speed variations demonstrate that the proposed method achieves the state-of-the-art performance in both identification and verification scenarios.