caa a22 4500 605447357 CHVBK 20210128100131.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s11042-013-1817-x doi (NATIONALLICENCE)springer-10.1007/s11042-013-1817-x Linear discriminant multi-set canonical correlations analysis (LDMCCA): an efficient approach for feature fusion of finger biometrics [Elektronische Daten] [Jialiang Peng, Qiong Li, Ahmed Abd El-Latif, Xiamu Niu] Feature fusion-based multimodal biometrics has become an increasing interest to many researchers in recent years, particularly for finger biometrics. There are, however, many challenges in fusing multiple feature sets, as the case with Canonical Correlation Analysis (CCA) and Multi-set Canonical Correlation Analysis (MCCA). How to extend them to fuse multiple feature sets is a significant problem in general. In this paper, we propose a novel multimodal finger biometric method, which provides feature fusion approach called linear discriminant multi-set canonical correlation analysis (LDMCCA). It combines finger vein, fingerprint, finger shape and finger knuckle print features of a single human finger. Compared with CCA and MCCA, LDMCCA contains the class information of the training samples and represents the fused features more efficiently and discriminatively in few dimensions. The experimental results on a merged multimodal finger biometric database show that LDMCCA is beneficial to fuse multiple features as well as achieves lower error rates than the existing approaches. Springer Science+Business Media New York, 2013 Multimodal biometrics nationallicence Finger nationallicence Feature fusion nationallicence Canonical correlation analysis nationallicence Peng Jialiang School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China aut Li Qiong School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China aut Abd El-Latif Ahmed Mathematics Department, Faculty of Science, Menoufia University, 32511, Shebin El-Koom, Egypt aut Niu Xiamu School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/13(2015-07-01), 4469-4486 1380-7501 74:13<4469 2015 74 11042 https://doi.org/10.1007/s11042-013-1817-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-013-1817-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Peng Jialiang School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China aut NATIONALLICENCE 700 1- Li Qiong School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China aut NATIONALLICENCE 700 1- Abd El-Latif Ahmed Mathematics Department, Faculty of Science, Menoufia University, 32511, Shebin El-Koom, Egypt aut NATIONALLICENCE 700 1- Niu Xiamu School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/13(2015-07-01), 4469-4486 1380-7501 74:13<4469 2015 74 11042