Faces Unveiled: A Deep Dive into Modern Face Detection and Recognition Techniques

Abstract

This paper provides a comprehensive overview of contemporary research in face detection, facial feature detection, and face recognition, categorizing methodologies into four primary types: knowledge-based, template matching, featurebased, and appearance-based. Analysis reveals a predominant focus on appearance-based techniques, particularly in recent studies. Literature showcases the increasing utilization of deep learning algorithms, such as CNN, DCNN, and Faster RCNN, to address challenges in face detection and recognition. Notably, these algorithms demonstrate high accuracy in complex scenarios, including variations in pose, scale, and occlusion. The overview highlights the effectiveness of knowledge-based methods in detecting facial features with low computational requirements, albeit with limited accuracy in complex situations. Appearance-based methods, particularly those employing deep learning, emerge as highly successful in face detection and recognition, achieving accuracy rates exceeding 99%. The integration of onestage and two-stage algorithms, coupled with traditional classifiers, underscores their efficacy. Researchers enhance accuracy through data augmentation, multi-task learning, and network acceleration techniques. The paper concludes that deep learning algorithms significantly impact face detection, recognition, and feature extraction, reflecting their pivotal role in advancing computer vision. The comprehensive review of 28 selected papers emphasizes the importance of continued research to further enhance these essential aspects of object detection.