A Comprehensive Review of Face Detection/Recognition Algorithms and Competitive Datasets to Optimize Machine Vision

Face recognition has emerged as one of the most prominent applications of image analysis and understanding, gaining considerable attention in recent years. This growing interest is driven by two key factors: its extensive applications in law enforcement and the commercial domain, and the rapid advancement of practical technologies. Despite the significant advancements, modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions, occlusion, and diverse facial postures. In such scenarios, human perception is still well above the capabilities of present technology. Using the systematic mapping study, this paper presents an in-depth review of face detection algorithms and face recognition algorithms, presenting a detailed survey of advancements made between 2015 and 2024. We analyze key methodologies, highlighting their strengths and restrictions in the application context. Additionally, we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications, size, diversity, and complexity. By analyzing these algorithms and datasets, this survey works as a valuable resource for researchers, identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.