Quality-Aware Estimation of Facial Landmarks in Video Sequences

Face alignment in video is a primitive step for facial image analysis. The accuracy of the alignment greatly depends on the quality of the face image in the video frames and low quality faces are proven to cause erroneous alignment. Thus, this paper proposes a system for quality aware face alignment by using a Supervised Decent Method (SDM) along with a motion based forward extrapolation method. The proposed system first extracts faces from video frames. Then, it employs a face quality assessment technique to measure the face quality. If the face quality is high, the proposed system uses SDM for facial landmark detection. If the face quality is low the proposed system corrects the facial landmarks that are detected by SDM. Depending upon the face velocity in consecutive video frames and face quality measure, two algorithms are proposed for correction of landmarks in low quality faces by using an extrapolation polynomial. Experimental results illustrate the competency of the proposed method while comparing with the state-of-the art methods including an SDM-based method (from CVPR-2013) and a very recent method (from CVPR-2014) that uses parallel cascade of linear regression (Par-CLR).