eng URI http://hdl.handle.net/10795/2937 βιομετρία Facial landmark detection is a crucial first step in facial analysis for biometrics and numerous other applications. However, it has proved to be a very challenging task due to the numerous sources of variation in 2D and 3D facial data. Although landmark detection based on descriptors of the 2D and 3D appearance of the face has been extensively studied, the fusion of such feature descriptors is a relatively under-studied issue. In this report, a novel generalized framework for combining facial feature descriptors is presented, and several feature fusion schemes are proposed and evaluated. The proposed framework maps each feature into a similarity score, combines the individual similarity scores into a resultant score, used to select the optimal solution for a queried landmark. The evaluation of the proposed fusion schemes for facial landmark detection clearly indicates that a quadratic distance to similarity mapping in conjunction with a root mean square rule for similarity fusion achieves the best performance in accuracy, efficiency, robustness and monotonicity. 27 pp. LOMv1.0 creator LOMv1.0 Scientific Coordinator LOMv1.0 Project Executing Organisation 2013-01 Digital Library of the Operational Programme "Education and Lifelong Learning" abstract types Text Facial landmarks Feature extraction Feature fusion Landmark detection 861991 application/pdf http://repository.edulll.gr/edulll/bitstream/10795/2937/2/2937_1.3_%ce%94%ce%97%ce%9c_1_1_13.pdf URI http://hdl.handle.net/10795/2937 LOMv1.0creator 2016-04-26T12:48:01Z LOMv1.0validator 2016-04-26T12:48:01Z LOMv1.0 gre no no Copyright EYD-EPEDBM (Operational Programme "Education and Lifelong Learning")