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.

翻译：步态识别是一种重要的人员身份识别生物特征技术，尤其在其它生理生物特征不适用或无效的场景中具有独特价值。本文针对步态识别面临的挑战，提出了一种提高其准确性与可靠性的新方法。该方法融合了多项先进技术：通过Mediapipe姿态估计模型获取序列化步态关键点，采用Procrustes分析进行对齐处理，并构建孪生双向门控循环单元-双堆栈神经网络架构以捕捉时序依赖关系。我们在大规模跨视角数据集上进行了广泛实验，验证了该方法的有效性，相较于其他模型取得了更高的识别准确率。该模型在CASIA-B、SZU RGB-D、OU-MVLP和Gait3D数据集上分别达到95.7%、94.44%、87.71%和86.6%的准确率。实验结果凸显了该方法在多领域实际应用中的潜力，表明其对步态识别领域的重要贡献。