Geometric Facial Components Feature Extraction for Facial Expression Recognition

Facial expression recognition is an active research challenge in computer vision and artificial intelligence since facial expressions contribute non-verbal information in human communication. Capturing facial features become an important phase in facial recognition systems. Finding suitable feature descriptor is essential to determine the recognition results. We propose a novel geometric feature extraction method which apply simple calculation techniques for facial components to ensure the robustness for each variation of pose. Unlike any other features which require more efforts in a transformation process, the proposed method efficiently works directly on pixels basis. We apply our proposed features into a facial expression recognition system and validate emotion results on extended Cohn Kanade (CK+) emotion dataset and gives accuracy rate 93.67%.