caa a22 4500 60615969X CHVBK 20210128100626.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00521-014-1783-x doi (NATIONALLICENCE)springer-10.1007/s00521-014-1783-x Finger-vein recognition with modified binary tree model [Elektronische Daten] [Tong Liu, Jianbin Xie, Wei Yan, Peiqin Li, Huanzhang Lu] Finger-vein recognition is an increasingly promising biometric identification technology in terms of its high identification accuracy and prominent security performance. The main challenge faced by finger-vein recognition is the low recognition performance caused by segmentation error and local difference. To tackle this challenge, a finger-vein recognition method with modified binary tree (MBT) model is proposed in this paper. MBT model is used to describe the relationship and spatial structure of vein branches quantitatively. Based on the MBT model, four stages including rough selection, model correction, segment matching, and comprehensive judgment are presented to achieve a robust matching for finger-vein. Experiments demonstrate that the proposed method can boost the performance of finger-vein recognition that is degraded by segmentation error and local difference. While maintaining low complexity, the proposed method achieves 0.12% equal error rate in the introduced dataset with 8,100 finger-vein images from 150 participants, which outperforms the state-of-the-art methods. The Natural Computing Applications Forum, 2014 Finger-vein recognition nationallicence Biometric identification nationallicence Pattern recognition nationallicence Liu Tong College of Electronic Science and Engineering, National University of Defense Technology, Kaifu District, Yan W Pond #47, 410073, Changsha, Hunan, People's Republic of China aut Xie Jianbin College of Electronic Science and Engineering, National University of Defense Technology, Kaifu District, Yan W Pond #47, 410073, Changsha, Hunan, People's Republic of China aut Yan Wei College of Electronic Science and Engineering, National University of Defense Technology, Kaifu District, Yan W Pond #47, 410073, Changsha, Hunan, People's Republic of China aut Li Peiqin College of Electronic Science and Engineering, National University of Defense Technology, Kaifu District, Yan W Pond #47, 410073, Changsha, Hunan, People's Republic of China aut Lu Huanzhang College of Electronic Science and Engineering, National University of Defense Technology, Kaifu District, Yan W Pond #47, 410073, Changsha, Hunan, People's Republic of China aut Neural Computing and Applications Springer London 26/4(2015-05-01), 969-977 0941-0643 26:4<969 2015 26 521 https://doi.org/10.1007/s00521-014-1783-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/s00521-014-1783-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Liu Tong College of Electronic Science and Engineering, National University of Defense Technology, Kaifu District, Yan W Pond #47, 410073, Changsha, Hunan, People's Republic of China aut NATIONALLICENCE 700 1- Xie Jianbin College of Electronic Science and Engineering, National University of Defense Technology, Kaifu District, Yan W Pond #47, 410073, Changsha, Hunan, People's Republic of China aut NATIONALLICENCE 700 1- Yan Wei College of Electronic Science and Engineering, National University of Defense Technology, Kaifu District, Yan W Pond #47, 410073, Changsha, Hunan, People's Republic of China aut NATIONALLICENCE 700 1- Li Peiqin College of Electronic Science and Engineering, National University of Defense Technology, Kaifu District, Yan W Pond #47, 410073, Changsha, Hunan, People's Republic of China aut NATIONALLICENCE 700 1- Lu Huanzhang College of Electronic Science and Engineering, National University of Defense Technology, Kaifu District, Yan W Pond #47, 410073, Changsha, Hunan, People's Republic of China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/4(2015-05-01), 969-977 0941-0643 26:4<969 2015 26 521