Automatic Face Detection System Research Articles

Geometric Analysis and YOLO Algorithm for Automatic Face Detection System in a Security Setting

Face detection is a computer technology that determines the location and size of the human face in an arbitrary digital image employed for authentication. Face recognition technology plays a vital role in network security, video compression, content indexing, and retrieval since "humans" are the focal point of many videos. Face recognition-based network access control makes it almost difficult for hackers to obtain a user’s "password" and improves user-friendliness in human-computer interaction. In this paper, an automatic face detection system that accurately detects human faces and ignores any other object that is not a human face using geometric analysis and the you-only-look-once (YOLO) algorithm is introduced. The system is able to predict the ages and genders of the faces detected. It also detects the facial landmarks of the faces and indicates the emotions of the faces detected. A sample of four faces is considered for testing the system; thus, accurately detecting gender and emotion but not age correctly. In all, the evaluation shows about 80% accuracy. With the results got, the system can support security and analytics. It can be used to get analytics in an event with a lot of attendees and can also be used to get a facial mapping of someone involved in a crime scene for security purposes.

Comparison of Real-Time Face Detection and Recognition Algorithms

With the phenomenal growth of video and image databases, there is a tremendous need for intelligent systems to automatically understand and examine information, as doing so manually is becoming increasingly difficult. The face is important in social interactions because it conveys information. Detecting a person's identity and feelings Humans do not have a great deal of ability to identify. Machines have different faces. As a result, an automatic face detection system is essential.in face recognition, facial expression recognition, head-pose estimation, and human–computer interaction, and so on Face detection is a computer technology that determines the location and size of a person's face. It also creates a digital image of a human face. Face detection has been a standout topic in the science field This paper provides an in-depth examination of the various techniques investigated for face detection in digital images. Various face challenges and applications. This paper also discusses detection. Detection features are also provided. In addition, we hold special discussions on the practical aspects of developing a robust face detection system, and finally. This paper concludes with several promising research directions for the future.

Face detection techniques: a review

With the marvelous increase in video and image database there is an incredible need of automatic understanding and examination of information by the intelligent systems as manually it is getting to be plainly distant. Face plays a major role in social intercourse for conveying identity and feelings of a person. Human beings have not tremendous ability to identify different faces than machines. So, automatic face detection system plays an important role in face recognition, facial expression recognition, head-pose estimation, human–computer interaction etc. Face detection is a computer technology that determines the location and size of a human face in a digital image. Face detection has been a standout amongst topics in the computer vision literature. This paper presents a comprehensive survey of various techniques explored for face detection in digital images. Different challenges and applications of face detection are also presented in this paper. At the end, different standard databases for face detection are also given with their features. Furthermore, we organize special discussions on the practical aspects towards the development of a robust face detection system and conclude this paper with several promising directions for future research.

FMRI evidence of neural abnormalities in the subcortical face processing system in ASD

Recent evidence suggests that a rapid, automatic face detection system is supported by subcortical structures including the amygdala, pulvinar, and superior colliculus. Early-emerging abnormalities in these structures may be related to reduced social orienting in children with autism, and subsequently, to aberrant development of cortical circuits involved in face processing. Our objective was to determine whether functional abnormalities in the subcortical face processing system are present in adults with autism spectrum disorders (ASD) during supraliminal fearful face processing. Participants included twenty-eight individuals with ASD and 25 controls group-matched on age, IQ, and behavioral performance. The ASD group met diagnostic criteria on the ADI-R, ADOS-G, and DSM-IV. Both the ASD and control groups showed significant activation in bilateral fusiform gyri. The control group exhibited additional significant responses in the right amygdala, right pulvinar, and bilateral superior colliculi. In the direct group comparison, the controls showed significantly greater activation in the left amygdala, bilateral fusiform gyrus, right pulvinar, and bilateral superior colliculi. No brain region showed significantly greater activation in the ASD group compared to the controls. Thus, basic rapid face identification mechanisms appear to be functional in ASD. However, individuals with ASD failed to engage the subcortical brain regions involved in face detection and automatic emotional face processing, suggesting a core mechanism for impaired socioemotional processing in ASD. Neural abnormalities in this system may contribute to early-emerging deficits in social orienting and attention, the putative precursors to abnormalities in social cognition and cortical face processing specialization.

Face detection: mapping human performance.

The recognition of faces has been the focus of an extensive body of research, whereas the preliminary and prerequisite task of detecting a face has received limited attention from psychologists. Four experiments are reported that address the question how we detect a face. Experiment 1 reveals that we use information from the scene to aid detection. In experiment 2 we investigated which features of a face speed the detection of faces. Experiment 3 revealed inversion effects and an interaction between the effects of blurring and reduction of contrast. In experiment 4 the sizes of effects of reversal of orientation, luminance, and hue were compared. Luminance was found to have the greatest effect on reaction time to detect faces. The results are interpreted as suggesting that face detection proceeds by a pre-attentive stage that identifies possible face regions, which is followed by a focused-attention stage that employs a deformable template. Comparisons are drawn with automatic face-detection systems.