Real time head tracking and face and eyes detection

This paper presents a novel algorithm to detect face and eyes in a reliable manner with a stereo camera. With the combination of stereo tracking method for object detection and tracking and structure-based method and example-based learning approach for face detection a real time and robust system is built. Human head detection is implemented with a stereo vision system, where a layered scenery is obtained from the depth image. With head-shoulder contour model applied to the disparity image, a fast head detection algorithm is developed. On the head detected, which also tells the rough scale of it, a hybrid face detection algorithm is proposed for face pattern detection and eyes location. By applying the face structure model and facial components model, face candidates in images are obtained for the later validating of face pattern. The support vector machine (SVM) is adopted here to select the good samples that form a classification boundary to classify face and non-face patterns.