Abstract

A robust way to discern facial mimics (facial muscle activity) in images of faces, insensitive to scale, pose, and occlusion, is still the key research challenge in automatic facial expression analysis domain. A practical method recognized as the most promising one for addressing this problem is through facial mimics analysis of multiple views of the face. However, portraits of faces or nearly frontal-views of faces constitute input data processed by current systems for automatic facial mimics analysis. To advance the existing technological framework upon which further research on automatic facial mimics analysis from multiple facial views can be based, we developed an automatic system as to analyze subtle changes in facial expressions based on profile-contour fiducial points in a profile-view video. A probabilistic classification method based on statistical modeling of the color and motion properties of the profile in the scene is proposed for tracking the profile face. From the segmented profile face, we extract the profile contour and from it, we extract 10 profile-contour fiducial points. Based on these face profile features, 20 individual facial-actions occurring alone or in a combination are recognized by a rule-based method. A recognition rate of 85% is obtained.