title: Improving subspace learning for facial expression recognition using person dependent and geometrically enriched training sets.
abstract: In this paper, the robustness of appearance-based subspace learning techniques in geometrical transformations of the images is explored. A number of such techniques are presented and tested using four facial expression databases. A strong correlation between the recognition accuracy and the image registration error has been observed. Although it is common-knowledge that appearance-based methods are sensitive to image registration errors, there is no systematic experiment reported in the literature. As a result of these experiments, the training set enrichment with translated, scaled and rotated images is proposed for confronting the low robustness of these techniques in facial expression recognition. Moreover, person dependent training is proven to be much more accurate for facial expression recognition than generic learning.
authors.affiliation: Aristotle University of Thessaloniki, Department of Informatics, Box 451, 54124 Thessaloniki, Greece.
authors.forename: Anastasios
authors.initials: A
authors.lastname: Maronidis
authors.forename: Dimitris
authors.initials: D
authors.lastname: Bolis
authors.forename: Anastasios
authors.initials: A
authors.lastname: Tefas
authors.forename: Ioannis
authors.initials: I
authors.lastname: Pitas
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D016208:Databases, Factual; D004644:Emotions; D005123:Eye; D005149:Facial Expression; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D021641:Recognition, Psychology / Q000502:physiology*; D015203:Reproducibility of Results