Abstract
Face detection, which is an effortless task for humans, is complex to perform on machines. The recent veer proliferation of computational resources is paving the way for frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is little attention paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. First, we explore a wide variety of the available face detection algorithms in five steps, including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all-inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of the neural network. We present detailed comparisons among the algorithms in all-inclusive and under sub-branches. We provide the strengths and limitations of these algorithms and a novel literature survey that includes their use besides face detection.
Highlights
Face detection is a computer vision problem that involves finding faces in images
Many face detection algorithms are reviewed, such as different statistical and neural network approaches, which were neglected in the earlier literature but gained popularity recently because of hardware development
The model was built by expanding the concepts behind the design and training of quantum neural networks (QNN), which is capable of detecting uncertainty in data classification by themselves
Summary
Face detection is a computer vision problem that involves finding faces in images. It is the initial step for many face-related technologies, for instance, face verification, face modeling, head pose tracking, gender and age recognition, facial expression recognition, and many more. Ashu et al followed the path of Erik and Low, adding face detection databases and application programming interfaces (APIs) for face detection Both the works are missing the recent efficient methods of face detection, such as subbranches of neural networks and statistical methods. Many recent research papers on face detection are available in the literature [5,6,7,8,9], which, closely related to our work, attempted to review face detection algorithms. Comparison among different sub-branches Implementation in other fields beside face detection In this survey, we present a structured classification of the related literature. Many face detection algorithms are reviewed, such as different statistical and neural network approaches, which were neglected in the earlier literature but gained popularity recently because of hardware development. ASMs can be classified into four groups: snakes, deformable template model (DTM), deformable part model (DPM), and point distribution model (PDM)
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