Amirgaliyev Beibut, Mussabek Miras, Rakhimzhanova Tomiris, Zhumadillayeva Ainur
Department of Computer Engineering, Astana IT University, Astana 010000, Kazakhstan.
Sensors (Basel). 2025 Feb 26;25(5):1410. doi: 10.3390/s25051410.
This paper provides a comprehensive analysis of recent developments in face recognition, tracking, identification, and person detection technologies, highlighting the benefits and drawbacks of the available techniques. To assess the state-of-art in these domains, we reviewed more than one hundred eminent journal articles focusing on current trends and research gaps in machine learning and deep learning methods. A systematic review using the PRISMA method helped us to generalize the search for the most relevant articles in this area. Based on our screening and evaluation procedures, we found and examined 142 relevant papers, evaluating their reporting compliance, sufficiency, and methodological quality. Our findings highlight essential methods of person detection, tracking and identification, and face recognition tasks, emphasizing current trends and illustrating a clear transition from classical to deep learning methods with existing datasets, divided by task and including statistics for each of them. As a result of this comprehensive review, we agree that the results demonstrate notable improvements. Still, there remain several key challenges like refining model robustness under varying environmental conditions, including diverse lighting and occlusion; adaptation to different camera angles; and ethical and legal issues related to privacy rights.
本文全面分析了人脸识别、跟踪、识别和人员检测技术的最新进展，突出了现有技术的优缺点。为了评估这些领域的技术现状，我们查阅了一百多篇著名期刊文章，重点关注机器学习和深度学习方法的当前趋势和研究空白。使用PRISMA方法进行的系统综述有助于我们在该领域全面搜索最相关的文章。基于我们的筛选和评估程序，我们找到了并审查了142篇相关论文，评估了它们的报告合规性、充分性和方法质量。我们的研究结果突出了人员检测、跟踪和识别以及人脸识别任务的基本方法，强调了当前趋势，并说明了从经典方法到深度学习方法在现有数据集上的明显转变，按任务划分并包括每个任务的统计数据。经过这次全面综述，我们认同结果显示出显著的进步。然而，仍然存在几个关键挑战，比如在不同环境条件下（包括不同光照和遮挡）提高模型的鲁棒性；适应不同的摄像头角度；以及与隐私权相关的伦理和法律问题。
