title: Facial expression recognition in image sequences using geometric deformation features and Support Vector Machines.
abstract: In this paper, two novel methods for facial expression recognition in facial image sequences are presented. The user has to manually place some of Candide grid nodes to face landmarks depicted at the first frame of the image sequence under examination. The grid-tracking and deformation system used, based on deformable models, tracks the grid in consecutive video frames over time, as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity. The geometrical displacement of certain selected Candide nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to a novel multiclass Support Vector Machine (SVM) system of classifiers that are used to recognize either the six basic facial expressions or a set of chosen Facial Action Units (FAUs). The results on the Cohn-Kanade database show a recognition accuracy of 99.7% for facial expression recognition using the proposed multiclass SVMs and 95.1% for facial expression recognition based on FAU detection.
authors.affiliation: Department of Informatics, Aristotle University of Thessaloniki, Greece. ekotsia@aiia.csd.auth.gr
authors.forename: Irene
authors.initials: I
authors.lastname: Kotsia
authors.forename: Ioannis
authors.initials: I
authors.lastname: Pitas
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005145:Face / Q000033:anatomy & histology*; D005149:Facial Expression; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D013382:Subtraction Technique; D014741:Video Recording / Q000379:methods