3D face pose estimation by a robust real time tracking of facial features

In this paper, we present a new 3D head pose estimating approach based on real-time facial feature tracking and facial feature recovering method which copes with the surrounding light variation and various occlusion. The major facial features are obtained by Haar-like feature detection along with AdaBoost learning from an input video image. The detected facial features are robustly tracked by optical flow with a template matching scheme which continuously compensates for losing track of the initially detected features in a sequence of input images. The head pose of an input face image can be obtained by evaluating 3D information of facial features from the detected 2D eye-points, nose and lip. From the experiments, the proposed method shows effectiveness in tracking and recovering facial features and produces reliable result in head pose estimation.