GaitSTAR: Spatial–Temporal Attention-Based Feature-Reweighting Architecture for Human Gait Recognition

Muhammad Bilal, He Jianbiao (), Husnain Mushtaq, Muhammad Asim (), Gauhar Ali and Mohammed ElAffendi
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Muhammad Bilal: School of Computer Science and Engineering, Central South University, Changsha 410083, China
He Jianbiao: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Husnain Mushtaq: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Muhammad Asim: EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
Gauhar Ali: EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
Mohammed ElAffendi: EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia

Mathematics, 2024, vol. 12, issue 16, 1-23

Abstract: Human gait recognition (HGR) leverages unique gait patterns to identify individuals, but the effectiveness of this technique can be hindered due to various factors such as carrying conditions, foot shadows, clothing variations, and changes in viewing angles. Traditional silhouette-based systems often neglect the critical role of instantaneous gait motion, which is essential for distinguishing individuals with similar features. We introduce the ”Enhanced Gait Feature Extraction Framework (GaitSTAR)”, a novel method that incorporates dynamic feature weighting through the discriminant analysis of temporal and spatial features within a channel-wise architecture. Key innovations in GaitSTAR include dynamic stride flow representation (DSFR) to address silhouette distortion, a transformer-based feature set transformation (FST) for integrating image-level features into set-level features, and dynamic feature reweighting (DFR) for capturing long-range interactions. DFR enhances contextual understanding and improves detection accuracy by computing attention distributions across channel dimensions. Empirical evaluations show that GaitSTAR achieves impressive accuracies of 98.5%, 98.0%, and 92.7% under NM, BG, and CL conditions, respectively, with the CASIA-B dataset; 67.3% with the CASIA-C dataset; and 54.21% with the Gait3D dataset. Despite its complexity, GaitSTAR demonstrates a favorable balance between accuracy and computational efficiency, making it a powerful tool for biometric identification based on gait patterns.

Keywords: computer vision; human gait recognition; feature aggregation; feature set transformation; feature fusion; dynamic feature reweighting; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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