A Comprehensive Review of Face Recognition Techniques, Trends, and Challenges

Face Recognition (FR) is the technology used to identify and verify individuals based on their facial features. In recent decades, FR plays a crucial role in various sectors, including security, healthcare, banking, and criminal identification. Numerous techniques for effective FR are currently under development, ranging from appearance to hybrid approaches. Most of the existing methods offer diverse solutions to describe a face image either by focusing on specific facial features or by considering the entire face. This study explores a various range of such techniques and challenges related to FR. The existing solutions were analysed with respect to various perspectives of inputs, viz., illumination, pose variation, facial expressions, occlusions, and aging, which led to the prominent implementation of FR systems. The primary contribution of this survey lies in the comprehensive review of state-of-the-art FR techniques and deriving the taxonomy of categorizing these methods into various classes which range from appearance to hybrid approaches. Moreover, the proposed detailed study highlights the significant features used by the most recent research developed in FR, also, provide a detailed classification of image and video-based FR methods, highlighting major advancements and core processing steps for handling huge volume of datasets. Moreover, the proposed study outlines the current trends in available datasets and emphasizing their enhancements. This survey also aims to provide a valuable resource for researchers and practitioners by offering insights into the latest developments and identifying open problems that require further investigation.