Efficient Face and Facial Feature Tracking Using Search Region Estimation

In this paper an intelligent and efficient combination of several methods are employed for face and facial feature tracking with the motivation for real time applications. Face tracking algorithm is based on color and connected component analysis. It is scale, pose and orientation invariant, and can be implemented in real time in controlled environments. The more challenging problem of facial feature tracking uses intensity based adaptive clustering on facial feature sub-images. New search region estimation for each sub-image is proposed. The technique employs facial expression aware eye sub-image prediction. The simulation results indicate that facial feature tracking is efficient with an average tracking rate of 99% with a three pixel range under different head movements such as translation, rotation, tilt, and scale changes. Furthermore it is robust under varying facial expressions and non-uniform illumination.