Occlusion handling in feature point tracking using ranked parts based models

A method for feature point tracking with partial occlusion handling is proposed. Occlusion causes distortion of entire face shape and not just the occluded part. To address this multiple models are learnt using regression, each aligning some part of the complete feature point set. A ranking SVM is then used to select the best feature points from among the aligned parts. The proposed method gives improved results compared to state of the art methods.