用人体关键点和双向网络提升步态识别准确率
A Bidirectional Siamese Recurrent Neural Network for Accurate Gait Recognition Using Body Landmarks
- 通过双向循环网络捕捉步态时序特征
- 在多个数据集上达到95.7%~86.6%的识别率
- 适合跨视角步态识别场景应用
步态识别是一种重要的生物特征技术，尤其在其他生理特征不可行或无效的情况下。本文针对步态识别挑战，提出一种新方法：利用Mediapipe姿态估计获取序列步态关键点，通过Procrustes分析对齐，再采用Siamese biGRU-dualStack神经网络建模时序依赖。在大规模跨视角数据集上进行大量实验，结果表明该方法显著提升识别性能，在CASIA-B、SZU RGB-D、OU-MVLP和Gait3D数据集上的准确率分别达到95.7%、94.44%、87.71%和86.6%，验证了其在实际应用中的潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。