A Survey on Classical and Modern Face Recognition Techniques

Abstract

During the previous few centuries, facial recognition systems have become a popular research topic. On account of its extraordinary success and vast social applications; it has attracted significant study attention from a wide range of disciplines in the last five years - including “computer-vision”, “artificial-intelligence”, and “machine-learning”. As with most face recognition systems, the fundamental goal involves recognizing a person's identity by means of images, video, data streams, and context information. As a result of our research; we've outlined some of the most important applications, difficulties, and trends in scientific and social domains. This research, the primary goal is to summarize modern facial recognition algorithms and to gain a general perceptive of how these techniques act on diverse datasets. Aside from that, we also explore some significant problems like illumination variation, position, aging, occlusion, cosmetics, scale, and background are some of the primary challenges we examine. In addition to traditional face recognition approaches, the most recent research topics such as sparse models, deep learning, and fuzzy set theory are examined in depth. There's also a quick discussion of basic techniques, as well as a more in-depth. As a final point, this research explores the future of facial recognition technologies and their possible importance in the emerging digital society.