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

arXiv: https://arxiv.org/abs/2412.03498 · HTML: https://mlsanity.com/paper/2412.03498/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 80 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition](https://mlsanity.com/paper/1811.06186/) (2018)
- [GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2101.11228/) (2021)
- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer](https://mlsanity.com/paper/2210.15491/) (2022)
- [Condition-Adaptive Graph Convolution Learning for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2308.06707/) (2023)
- [Sequential convolutional network for behavioral pattern extraction in gait recognition](https://mlsanity.com/paper/2104.11473/) (2021)
- [Human Activity Recognition Using Multichannel Convolutional Neural Network](https://mlsanity.com/paper/2101.06709/) (2021)
- [Gait Events Prediction using Hybrid CNN-RNN-based Deep Learning models through a Single Waist-worn Wearable Sensor](https://mlsanity.com/paper/2203.00503/) (2022)
- [Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework](https://mlsanity.com/paper/2208.05034/) (2022)

## Built on by (most cited first, 1 of 1)

- [Explainable Gait Abnormality Detection Using Dual-Dataset CNN-LSTM Models](https://mlsanity.com/paper/2509.16472/) (2025)

## Abstract (arXiv)

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.