Automatic assessment of facial expressions using fiducial points

Abstract

Facial expressions play an important role in human communication. Accurate recognition of facial expressions is important to understand nonverbal communication. Many tools have been developed but to recognize facial impressions in real life communications independent of lighting conditions, posture, occlusion and different intensities is still an unsolved problem. In the underlying paper we researched facial movements in daily interaction by tracking painted markers on the face to assess the displayed facial expressions. We analysed recordings of text based communication and computed the dictionary of emotional facial expressions and nonverbal grammatical rules.