Dual Attention Embedded Inception Framework for Robust Biometric Gait Recognition
TL;DR
Results validate the effectiveness of attention-driven multi-scale fusion for gait recognition and highlight the potential of AttIncGait for real-world biometric identification and mobility analysis.
Abstract
Gait recognition has emerged as an important biometric modality due to its non-invasive nature and suitability for surveillance and security applications. However, achieving robustness under real-world variations in viewpoint, clothing, and carrying conditions remains a significant challenge. This paper introduces \textbf{AttIncGait}, a deep learning framework that integrates Inception-based multi-scale feature extraction with dual-path attention for effective gait recognition. Unlike prior methods that treat attention as an auxiliary or late-stage refinement, our approach embeds spatial and channel attention directly within Inception modules, enabling simultaneous multi-scale representation and adaptive relevance weighting. This structural integration enhances discriminative capability while preserving computational efficiency. Experiments on CASIA-B and OU-MVLP datasets demonstrate state-of-the-art performance: 97.5% accuracy on OU-MVLP and a 2.6% improvement over the best existing method under clothing variation in CASIA-B. Ablation studies further reveal that spatial and channel attention individually improve accuracy, while their joint integration yields an overall +8.5% gain on OU-MVLP. These results validate the effectiveness of attention-driven multi-scale fusion for gait recognition and highlight the potential of AttIncGait for real-world biometric identification and mobility analysis.