title: A statistical method for 2-D facial landmarking.
abstract: Many facial-analysis approaches rely on robust and accurate automatic facial landmarking to correctly function. In this paper, we describe a statistical method for automatic facial-landmark localization. Our landmarking relies on a parsimonious mixture model of Gabor wavelet features, computed in coarse-to-fine fashion and complemented with a shape prior. We assess the accuracy and the robustness of the proposed approach in extensive cross-database conditions conducted on four face data sets (Face Recognition Grand Challenge, Cohn-Kanade, Bosphorus, and BioID). Our method has 99.33% accuracy on the Bosphorus database and 97.62% accuracy on the BioID database on the average, which improves the state of the art. We show that the method is not significantly affected by low-resolution images, small rotations, facial expressions, and natural occlusions such as beard and mustache. We further test the goodness of the landmarks in a facial expression recognition application and report landmarking-induced improvement over baseline on two separate databases for video-based expression recognition (Cohn-Kanade and BU-4DFE).
authors.affiliation: Intelligent Systems Lab Amsterdam, Informatics Institute, University of Amsterdam, 1098 XH Amsterdam, The Netherlands. h.dibeklioglu@uva.nl
authors.forename: Hamdi
authors.initials: H
authors.lastname: Dibeklioğlu
authors.forename: Albert Ali
authors.initials: AA
authors.lastname: Salah
authors.forename: Theo
authors.initials: T
authors.lastname: Gevers
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D016208:Databases, Factual; D005145:Face / Q000033:anatomy & histology*; D005149:Facial Expression; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014741:Video Recording