Abstract: Gait recognition is a rapidly evolving long-range biometric identification technique that has wide applications and advantages in various scenarios,including long distances,non-intrusive setups,and cross-view angles.Traditional biometrics identification technique,such as fingerprint recognition and facial recognition,often require close proximity or specific conditions to be effective,while gait recognition technology breaks through these limitations,making it possible to identify individuals in a wider range of environments.Previous research predominantly employed lightweight neural networks for gait feature extraction and achieved significant progress on popular datasets like CASIA-B,which feature cross-view angles and varying attires.However,experimental results indicate a substantial decline in recognition accuracy when simply stacking neural network layers on the CASIA-B dataset.A deep gait recognition network has been proposed,incorporating the relative position encoding transformer module.This module aims to avoid the pitfall of “local feature association"”and enables continuous learning of temporal features within gait sequences.Compared to current mainstream approaches,the proposed method has garnered enhanced identification precision across indoor environments,as exemplified by the CASIA-B and OUMVLP datasets,alongside outdoor settings typified by the Gait3D dataset.Especially in the task of clothes changing,wherein our method surpasses benchmark approaches by of 1.9%,achieving a recognition rate of 85.5%.

Key words: Gait recognition mechanism, Self-attention mechanism, Relative position modeling, Pattern recognition, Deep network