A Review of Machine Learning and Deep Learning Methods for Person Detection, Tracking and Identification, and Face Recognition with Applications

Beibut Amirgaliyev, Miras Mussabek, Tomiris Rakhimzhanova, Ainur Zhumadillayeva

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.