| dc.description.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. | en_US |
