A survey on fingerprint minutiae-based local matching for verification and identification: Taxonomy and experimental evaluation

被引:102
作者
Peralta, Daniel [1 ]
Galar, Mikel [3 ]
Triguero, Isaac [1 ,4 ]
Paternain, Daniel [3 ]
Garcia, Salvador [1 ,2 ]
Barrenechea, Edurne [3 ]
Benitez, Jose M. [1 ]
Bustince, Humberto [3 ]
Herrera, Francisco [1 ]
机构
[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain
[2] King Abdulaziz Univ, Fac Comp & Informat Technol North Jeddah, Jeddah 21589, Saudi Arabia
[3] Univ Publ Navarra, Dept Automat & Comp, Pamplona, Spain
[4] Inflammat Res Ctr, Resp Med GE01, B-9052 Zwijnaarde, Belgium
关键词
Biometrics; Fingerprint verification; Fingerprint identification; Local matching; Minutiae; STATISTICAL COMPARISONS; IMAGE-ENHANCEMENT; GLOBAL ALIGNMENT; SINGULAR POINTS; ALGORITHM; ORIENTATION; SYSTEM; REPRESENTATION; RECOGNITION; FEATURES;
D O I
10.1016/j.ins.2015.04.013
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Fingerprint recognition has found a reliable application for verification or identification of people in biometrics. Globally, fingerprints can be viewed as valuable traits due to several perceptions observed by the experts; such as the distinctiveness and the permanence on humans and the performance in real applications. Among the main stages of fingerprint recognition, the automated matching phase has received much attention from the early years up to nowadays. This paper is devoted to review and categorize the vast number of fingerprint matching methods proposed in the specialized literature. In particular, we focus on local minutiae-based matching algorithms, which provide good performance with an excellent trade-off between efficacy and efficiency. We identify the main properties and differences of existing methods. Then, we include an experimental evaluation involving the most representative local minutiae-based matching models in both verification and evaluation tasks. The results obtained will be discussed in detail, supporting the description of future directions. (C) 2015 Elsevier Inc. All rights reserved.
引用
收藏
页码:67 / 87
页数:21
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