Facial action detection from dual-view static face images

摘要

This work presents an automatic system that we developed for automatic recognition of facial gestures (facial muscle activity) from static images of combined frontal-and profile-view of the face. For the frontal view, the face region is subjected to multi-detector processing which per facial component (eyes, eyebrows, mouth), generates a spatial sample of its contour. A set of 19 frontal-face feature points is then extracted from the spatially sampled contours of the facial features. For the profile view, 10 feature points are extracted from the contour of the face-profile region. Based on these 29 points, 29 individual facial muscle action units (AUs) occurring alone or in combinations in an input dual-view image are recognized using a rule-based reasoning. With each scored AU, the utilized algorithm associates a factor denoting the certainty with which the pertinent AU has been scored. A recognition rate of 86% is achieved.