Summary of A Bidirectional Siamese Recurrent Neural Network For Accurate Gait Recognition Using Body Landmarks, by Proma Hossain Progga et al.
A Bidirectional Siamese Recurrent Neural Network for Accurate Gait Recognition Using Body Landmarks
by Proma Hossain Progga, Md. Jobayer Rahman, Swapnil Biswas, Md. Shakil Ahmed, Arif Reza Anwary, Swakkhar Shatabda
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to improve the accuracy and reliability of gait recognition, a biometric technique for person identification. It leverages advanced techniques such as Mediapipe pose estimation, Procrustes analysis, and a Siamese biGRU-dualStack Neural Network architecture to capture temporal dependencies in gait patterns. The proposed method is evaluated on large-scale cross-view datasets, achieving high recognition accuracy compared to other models, with accuracies ranging from 86.6% to 95.7%. The results demonstrate the potential applications of the proposed method in various practical domains, making a significant contribution to the field of gait recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gait recognition is a way to identify people by how they walk. It’s useful when other ways of identifying people aren’t working. The paper presents a new approach that makes this technique more accurate and reliable. They use special algorithms and techniques, like measuring body poses and aligning data. They tested their method on lots of different walking patterns and found it was very good at recognizing people, with accuracy rates ranging from 86% to 96%. This could be useful in many real-life situations. |
Keywords
» Artificial intelligence » Neural network » Pose estimation
