eng URI http://hdl.handle.net/10795/2942 βιομετρία 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. 13 pp. LOMv1.0 creator LOMv1.0 Scientific Coordinator LOMv1.0 Project Executing Organisation 2013-07 Digital Library of the Operational Programme "Education and Lifelong Learning" abstract types Text Face models Landmark detection Shape index Spin images http://www.ncbi.nlm.nih.gov/pubmed/23681986 1922593 application/pdf http://repository.edulll.gr/edulll/bitstream/10795/2942/2/2942_1.3_%ce%94%ce%97%ce%9c_1_7_13.pdf URI http://hdl.handle.net/10795/2942 LOMv1.0creator 2016-04-27T07:04:16Z LOMv1.0validator 2016-04-27T07:04:16Z LOMv1.0 gre no no Copyright EYD-EPEDBM (Operational Programme "Education and Lifelong Learning")