Face Detection Technology

Recognition and analysis of human faces can be widely used in such fields as personal identification recognition, safety inspection, human-computer interaction, expression analysis and lip-reading. As the basis, necessity and prerequisite of human face recognition and analysis, human face detection with the help of computer attracted people's attention at a very early time . Along with the popularization of computer application , the improvement of computer performance and achievements in image procession and pattern recognition , human-face related applications are getting nearer and nearer to practical use , thus resulting in more and more emphasis over research for human face detection .Facial features detection and localization , an important technique in human face analysis , is specialized in searching for facial features (eyes , nose , mouth , ears , etc) with in a given region in an image or image sequence . It finds applications in various areas , such as face detection , face recognition , gesture recognition , expression recognition , face image compression and reconstruction , and face cartoon . This thesis attempts to give an overview of the latest development in this field by classifying the newly proposed methods into five categories , namely the ones based on knowledge , geometry information ,color .appearance and relative location . All these methods appeared in the related papers or works published on international , as well as Chinese , journals and conference proceedings in recent years , Then their performances in accuracy , robustness and computational expense are roughly estimated and compared , and some discussions about the criteria on which the estimation and comparison are based provided .In this paper, a self-adaptive skin color detection algorithm for color images based on HSV color space, which is composed of skin color segmentation using H threshold, relative significance filter and self-adaptive region merging, is presented. First, a threshold of H is used for segmenting skin color regions in HSV color space, and then we use relative significance filter and self-adaptive region merging to process the skin color regions which have been segmented. In the end, transforming the color images within the candidate face regions into gray images and comparing them with the...