caa a22 4500 44585149X CHVBK 20180317145414.0 cr unu---uuuuu 170323e20111001xx s 000 0 eng 10.1007/s11227-010-0533-9 doi (NATIONALLICENCE)springer-10.1007/s11227-010-0533-9 3D face recognition based on sparse representation [Elektronische Daten] [Hengliang Tang, Yanfeng Sun, Baocai Yin, Yun Ge] In this paper, we present a novel 3D face recognition algorithm based on the sparse representation. First, a 3D face normalization approach is proposed to deal with the raw faces. Then, three types of facial geometrical features are extracted to describe the 3D faces. Meanwhile, in order to guarantee the feasibility of the sparse representation framework and promote the recognition efficiency, a novel feature ranking scheme based on Fisher linear discriminant analysis (FLDA) is designed to arrange the facial descriptors. Finally, the sparse representation framework is used to collect all the face features, and it addresses the recognition task. The experiments tested on the BJUT-3D and FRGC v2.0 databases demonstrate the validity of the proposed 3D face recognition algorithm, and the necessity of the FLDA ranking scheme in the sparse representation framework. Springer Science+Business Media, LLC, 2010 3D face recognition nationallicence Sparse representation nationallicence Facial geometrical features nationallicence FLDA ranking scheme nationallicence Tang Hengliang Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, 100124, Beijing, P.R. China aut Sun Yanfeng Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, 100124, Beijing, P.R. China aut Yin Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, 100124, Beijing, P.R. China aut Ge Yun Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, 100124, Beijing, P.R. China aut The Journal of Supercomputing Springer US; http://www.springer-ny.com 58/1(2011-10-01), 84-95 0920-8542 58:1<84 2011 58 11227 https://doi.org/10.1007/s11227-010-0533-9 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11227-010-0533-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Tang Hengliang Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, 100124, Beijing, P.R. China aut NATIONALLICENCE 700 1- Sun Yanfeng Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, 100124, Beijing, P.R. China aut NATIONALLICENCE 700 1- Yin Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, 100124, Beijing, P.R. China aut NATIONALLICENCE 700 1- Ge Yun Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, 100124, Beijing, P.R. China aut NATIONALLICENCE 773 0- The Journal of Supercomputing Springer US; http://www.springer-ny.com 58/1(2011-10-01), 84-95 0920-8542 58:1<84 2011 58 11227 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer