Mean-shift based mixture model for face detection in color image

Human face detection is a challenging task under different lighting conditions. In this paper, we propose an efficient and reliable algorithm to detect human faces in an image. In our algorithm, skin-colored pixels under various lighting conditions are identified by using a region-based approach. Within the detected skin-color regions, a ratio method is proposed to determine possible eye candidates. Two eye candidates form a possible face region, which is then verified by means of a two-stage procedure with an eigenmask. Experimental results based on the HHI MPEG-7 face database show that this face detection algorithm is efficient and reliable under different lighting conditions.