Abstract: As one of the most successful applications of image analysis and understanding, face recognition has recently gained significant attention. Over the last ten years or so, it has become a popular area of research in computer vision and one of the most successful applications of image analysis and understanding. A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. Biometric face recognition, otherwise known as Automatic Face Recognition (AFR), is a particularly attractive biometric approach, since it focuses on the same identifier that humans use primarily to distinguish one person from another: their “faces”. One of its main goals is the understanding of the complex human visual system and the knowledge of how humans represent faces in order to discriminate different identities with high accuracy.
Human face and facial feature detection have attracted a lot of attention because of their wide applications, such as face recognition, face image database management and human-computer interaction. So it is of interest to develop a fast and robust algorithm to detect the human face and facial features. This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates.
Keywords: Haar-like features, Integral Images, LEM of image- Line Edge Map, Mask size
DECREASING VOLUME OF FACE IMAGES DATABASE AND EFFICIENT FACE DETECTION ALGORITHM
Grigor A. Poghosyan and Hakob G. Sarukhanyan