Face detection by integrating multiresolution-based watersheds and a skin-color model

In this paper, we propose a method to automatically segment out a human's face from a given image that consists of head-and shoulder views of humans against complex backgrounds in videoconference video sequences. The proposed method consists of two steps: region segmentation and facial region detection. In the region segmentation, the input image is segmented using multiresolution-based watershed algorithms segmenting the image into an appropriate set of arbitrary regions. Then, to merge the regions forming an object, we use spatial similarity between two regions since the regions forming an object share some common spatial characteristics. In the facial region detection, the facial regions are identified from the results of region segmentation using a skin-color model. The results of the multiresolution-based watersheds image segmentation and facial region detection are integrated to provide facial regions with accurate and closed boundaries. In our experiments, the proposed algorithm detected 87-94% of the faces, including frames from videoconference images and new video. The average run time ranged from 0.23-0.34 see per frame. This method has been successfully assessed using several test video sequences from MPEG-4 as well as MPEG-7 videoconferences.