Normalization Refinement for Occluded Gait Recognition

Gait recognition is a biometric technology that identifies individuals based on their walking posture and motion. It has significant potential for applications such as criminal investigations and security systems. In silhouette-based gait recognition, silhouette normalization is a key component that enhances recognition accuracy. However, in scenarios where partial occlusions frequently occur, normalization can sometimes negatively affect recognition performance due to inappropriate adjustments. To address this issue, we propose a normalization refinement network based on spatial transformer networks, designed to mitigate the adverse effects of improper normalization. To validate the effectiveness of our method, we apply it to multiple gait recognition models and evaluate its performance under various occlusion scenarios using the OU-MVLP dataset. Experimental results demonstrate that our approach improves recognition accuracy across different occlusion conditions and multiple recognition models.