Face Detection Using Skin Likelihood with Mean Shift Tracking For Digital Video Processing?

Face detection is an important early step in many computer vision systems. By using pixel-wise detectors, spatial analysis of skin probability and skin regions segmentation, a new method for face detection is introduced. In this project, we proposed and implemented a modified self-organizing mixture network (SOMN) which specifies the distribution of objects in image, skin and non-skin color model based on sample chrominance values, skin likely-hood to exactly identify skin region of interest from image and finally Bayesian Decision Rule is applied to specify c as skin color or non-skin color. Finally, we present results of an extensive experimental study which clearly indicate high competitiveness of the proposed method and its relevance to gesture recognition.