Head pose estimation based on detecting facial features

Abstract

Head pose estimation is recently a more popular area of research. Challenging conditions, such as extreme pose, lighting, and occlusion, has historically hampered traditional, model-based methods. This paper presents a proposal of an integrated method for head pose estimation based on face detection and tracking. This method first locates certain facial features and based on their relative locations determine the head pose, the head pose estimated using coordinates of both eyes and a mouth relative to the nose as the origin of the coordinate system. The nose position is set up as the origin. The coordinates of the other parts defined from the origin, the distance between the face parts normalized so that the coordinates are independent of the image size. For facial feature detection from the detected face region, Haar-like feature utilized along with AdaBoost learning, the Adaboost learning algorithm used for creating optimized learning data. From the experiments, the proposed approach shows robustness in face and facial feature detection and eventually produces better results in estimating head pose rather than simply using Haar-like feature for both face and facial feature detection. The computational cost is low because it uses only those three points.