关键词: 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 the era of 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. Current gait recognition algorithms mainly use supervised learning, which requires extensive labeled data, and faces practical challenges. Unsupervised learning, which can automatically extract intrinsic features of data without labels, aligns better with real-world needs. This paper 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 on three typical datasets: CASIA-B, OU-MVLP and OU-ISIR LP. This paper also summarizes 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 development in the gait recognition field.

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