title: 3D facial landmark detection under large yaw and expression variations.
abstract: A 3D landmark detection method for 3D facial scans is presented and thoroughly evaluated. The main contribution of the presented method is the automatic and pose-invariant detection of landmarks on 3D facial scans under large yaw variations (that often result in missing facial data), and its robustness against large facial expressions. Three-dimensional information is exploited by using 3D local shape descriptors to extract candidate landmark points. The shape descriptors include the shape index, a continuous map of principal curvature values of a 3D object's surface, and spin images, local descriptors of the object's 3D point distribution. The candidate landmarks are identified and labeled by matching them with a Facial Landmark Model (FLM) of facial anatomical landmarks. The presented method is extensively evaluated against a variety of 3D facial databases and achieves state-of-the-art accuracy (4.5-6.3 mm mean landmark localization error), considerably outperforming previous methods, even when tested with the most challenging data.
authors.affiliation: Department of Informatics and Telecommunications, University of Athens, 17584 Ilisia, Greece. takis@antinoos.gr
authors.forename: Panagiotis
authors.initials: P
authors.lastname: Perakis
authors.forename: Georgios
authors.initials: G
authors.lastname: Passalis
authors.forename: Theoharis
authors.initials: T
authors.lastname: Theoharis
authors.forename: Ioannis A
authors.initials: IA
authors.lastname: Kakadiaris
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D016208:Databases, Factual; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*