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-theart
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).