A Bidirectional Siamese Recurrent Neural Network for Accurate Gait Recognition Using Body Landmarks

TL;DR

This paper introduces a novel bidirectional Siamese recurrent neural network that leverages body landmarks and alignment techniques to significantly improve gait recognition accuracy across multiple large-scale datasets.

Contribution

It presents a new neural network architecture combined with pose estimation and alignment methods for more reliable gait recognition.

Findings

Achieved over 95% accuracy on CASIA-B dataset.

Demonstrated robustness across multiple cross-view datasets.

Outperformed existing gait recognition models.

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 Gait3D datasets 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.