关键词: 计算机技术, 步态识别, 数字图像处理, 神经网络, 无监督学习, 机器学习, 生物特征识别

Abstract: In today's fast-paced development of optical technologies, gait analysis has become increasingly significant due to its non-contact, non-invasive nature, resistance to impersonation, and suitability for long-distance data capture. While current gait recognition algorithms mainly use supervised learning, which requires extensive labeled data, this approach faces practical challenges. Unsupervised learning, which can automatically extract data's intrinsic features without needing labels, aligns better with real-world needs. This paper comprehensively reviews the development and trends of unsupervised learning in gait recognition by collating relevant research work. Initially, it outlines commonly used gait datasets, their standard creation methods, and mainstream evaluation metrics. It then delves into the current research on unsupervised learning for gait recognition, detailing approaches from four perspectives: GAN-based methods, clustering-based methods, unsupervised domain adaptation techniques, and other approaches. The performance of major unsupervised algorithms is compared across three typical datasets: CASIA-B, OU-MVLP, and OU-ISIR LP. The paper also summarizes and discusses the research focus of each direction, and comments on the existence of cross-cutting researches, so as to provide ideas for future research. Lastly, it analyzes the challenges faced by unsupervised gait recognition algorithms and forecasts potential future developments in the gait recognition field.

Key words: computer technology, gait recognition, digital image processing, neural network, unsupervised learning, machine learning, bio-metric identification