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

• URL: http://arxiv.org/abs/2412.03498v2
• Date: Thu, 05 Dec 2024 03:47:49 GMT
• Title: A Bidirectional Siamese Recurrent Neural Network for Accurate Gait Recognition Using Body Landmarks
• Authors: Proma Hossain Progga, Md. Jobayer Rahman, Swapnil Biswas, Md. Shakil Ahmed, Arif Reza Anwary, Swakkhar Shatabda,
• Abstract summary: 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. Experiments were conducted on large-scale cross-view datasets to demonstrate the effectiveness of the approach.
• Score: 1.4019041243188557
• License:
• 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.

Related papers

• The Paradox of Motion: Evidence for Spurious Correlations in Skeleton-based Gait Recognition Models [4.089889918897877]
  This study challenges the prevailing assumption that vision-based gait recognition relies primarily on motion patterns. We show through a comparative analysis that removing height information leads to notable performance degradation. We propose a spatial transformer model processing individual poses, disregarding any temporal information, which achieves unreasonably good accuracy.
  arXiv  Detail & Related papers  (2024-02-13T09:33:12Z)
• Augmentation is AUtO-Net: Augmentation-Driven Contrastive Multiview Learning for Medical Image Segmentation [3.1002416427168304]
  This thesis focuses on retinal blood vessel segmentation tasks. It provides an extensive literature review of deep learning-based medical image segmentation approaches. It proposes a novel efficient, simple multiview learning framework.
  arXiv  Detail & Related papers  (2023-11-02T06:31:08Z)
• Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition [20.655367200006076]
  This study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals. The graph mixup technique is employed to enhance latent connected edges and mitigate noisy label issues. We evaluate our approach on two widely used datasets, namely SEED and SEEDIV, for emotion recognition tasks.
  arXiv  Detail & Related papers  (2023-10-22T03:47:11Z)
• Distillation-guided Representation Learning for Unconstrained Gait Recognition [50.0533243584942]
  We propose a framework, termed GAit DEtection and Recognition (GADER), for human authentication in challenging outdoor scenarios. GADER builds discriminative features through a novel gait recognition method, where only frames containing gait information are used. We evaluate our method on multiple State-of-The-Arts(SoTA) gait baselines and demonstrate consistent improvements on indoor and outdoor datasets.
  arXiv  Detail & Related papers  (2023-07-27T01:53:57Z)
• Learning Diversified Feature Representations for Facial Expression Recognition in the Wild [97.14064057840089]
  We propose a mechanism to diversify the features extracted by CNN layers of state-of-the-art facial expression recognition architectures. Experimental results on three well-known facial expression recognition in-the-wild datasets, AffectNet, FER+, and RAF-DB, show the effectiveness of our method.
  arXiv  Detail & Related papers  (2022-10-17T19:25:28Z)
• Multi-Modal Human Authentication Using Silhouettes, Gait and RGB [59.46083527510924]
  Whole-body-based human authentication is a promising approach for remote biometrics scenarios. We propose Dual-Modal Ensemble (DME), which combines both RGB and silhouette data to achieve more robust performances for indoor and outdoor whole-body based recognition. Within DME, we propose GaitPattern, which is inspired by the double helical gait pattern used in traditional gait analysis.
  arXiv  Detail & Related papers  (2022-10-08T15:17:32Z)
• Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline [95.88825497452716]
  Gait benchmarks empower the research community to train and evaluate high-performance gait recognition systems. GREW is the first large-scale dataset for gait recognition in the wild. SPOSGait is the first NAS-based gait recognition model.
  arXiv  Detail & Related papers  (2022-05-05T14:57:39Z)
• HEATGait: Hop-Extracted Adjacency Technique in Graph Convolution based Gait Recognition [0.0]
  HEATGait is a gait recognition system that improves the existing multi-scale convolution graph by efficient hop-extraction technique to alleviate the issue. We propose a powerful feature extractor that utilizes ResG to achieve state-of-the-art performance in model-based gait recognition on the CASIA-BCN gait dataset.
  arXiv  Detail & Related papers  (2022-04-21T16:13:58Z)
• TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition [77.77786072373942]
  This paper proposes a Transferable Neighborhood Discovery (TraND) framework to bridge the domain gap for unsupervised cross-domain gait recognition. We design an end-to-end trainable approach to automatically discover the confident neighborhoods of unlabeled samples in the latent space. Our method achieves state-of-the-art results on two public datasets, i.e., CASIA-B and OU-LP.
  arXiv  Detail & Related papers  (2021-02-09T03:07:07Z)
• UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders [81.5490760424213]
  We propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network.
  arXiv  Detail & Related papers  (2020-04-13T04:12:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.