A bidirectional Siamese recurrent neural network for accurate gait recognition using body landmarks

Progga, Proma Hossain; Rahman, Md. Jobayer; Biswas, Swapnil; Ahmed, Md. Shakil; Anwary, Arif Reza; Shatabda, Swakkhar

Abstract

Gait recognition is a significant biometric technique for person identification, particularly in scenarios where other physiological biometrics are impractical or ineffective. In this paper, we address the challenges associated with gait recognition and present a novel approach to improve its accuracy and reliability. The proposed method leverages advanced techniques, including sequential gait landmarks obtained through the Mediapipe pose estimation model, Procrustes analysis for alignment, and a Siamese biGRU-dualStack Neural Network architecture for capturing temporal dependencies. Extensive experiments were conducted on large-scale cross-view datasets to demonstrate the effectiveness of the approach, achieving high recognition accuracy compared to other models. The model demonstrated accuracies of 95.7%, 94.44%, 87.71%, and 86.6% on CASIA-B, SZU RGB-D, OU-MVLP, and Gait3Ddatasets respectively. The results highlight the potential applications of the proposed method in various practical domains, indicating its significant contribution to the field of gait recognition.

Citation

Progga, P. H., Rahman, M. J., Biswas, S., Ahmed, M. S., Anwary, A. R., & Shatabda, S. (2024). A bidirectional Siamese recurrent neural network for accurate gait recognition using body landmarks. Neurocomputing, 605, Article 128313. https://doi.org/10.1016/j.neucom.2024.128313

• Journal Article Type: Article
• Acceptance Date: Aug 3, 2024
• Online Publication Date: Aug 8, 2024
• Publication Date: 2024-11
• Deposit Date: Jan 29, 2025
• Journal: Neurocomputing
• Print ISSN: 0925-2312
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 605
• Article Number: 128313
• DOI: https://doi.org/10.1016/j.neucom.2024.128313
• Keywords: Gait recognition, Biometrics, Person identification, Gait landmarks, Procrustes analysis, Siamese biGRU-dualStack neural network
• Public URL: http://researchrepository.napier.ac.uk/Output/3786818