Automated Facial Pose Extraction From Video Sequences Based on Mutual Information
Estimation of the facial pose in video sequences is one of the major issues in many vision systems such as face-based biometrics, scene understanding for humans, and others. The proposed method uses a novel pose estimation algorithm based on mutual information to extract any required facial poses from video sequences. The method extracts the poses automatically and classifies them according to view angle. Experimental results on the XM2VTS video database and on a new database created for the needs of this research indicated a pose classification rate of 99.2% while it was shown that it outperforms a principal component analysis reconstruction method that was used as a benchmark.