Enhancing Gait Recognition with Attention-Based Spatial-Temporal Deep Learning: The GaitDeep Framework

Gait, an individual's unique walking style, serves as an effective biometric tool for surveillance. Unlike fingerprints or iris scans, gait is observable from a distance without the subject's awareness, making it ideal for security applications. CNNs struggle with video variability, affecting gait recognition. This study introduces GaitDeep, a spatial-temporal refinement using a deep dense network. It integrates attention-enhanced spatial extraction with a two-directional LSTM-based temporal module to prioritize key segments. Evaluated on the OU-ISIR, OU-MVLP, and CASIA-B datasets, GaitDeep achieves accuracies of 95.1%, 0.96%, and 98.10%, respectively, outperforming state-of-the-art methods and establishing a new benchmark for gait recognition.