A hybrid fingerprint matcher

被引:221
作者
Ross, A [1 ]
Jain, A
Reisman, J
机构
[1] Michigan State Univ, Dept Comp Sci & Engn, E Lansing, MI 48824 USA
[2] Siemens Corp Res Inc, Princeton, NJ 08540 USA
关键词
fingerprints; verification; identification; Fourier transform; texture; ridge flow; gabor filter;
D O I
10.1016/S0031-3203(02)00349-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Most fingerprint matching systems rely on the distribution of minutiae on the fingertip to represent and match fingerprints. While the ridge flow pattern is generally used for classifying fingerprints, it is seldom used for matching. This paper describes a hybrid fingerprint matching scheme that uses both minutiae and ridge flow information to represent and match fingerprints. A set of 8 Gabor filters, whose spatial frequencies correspond to the average inter-ridge spacing in fingerprints, is used to capture the ridge strength at equally spaced orientations. A square tessellation of the filtered images is then used to construct an eight-dimensional feature map, called the ridge feature map. The ridge feature map along with the minutiae set of a fingerprint image is used for matching purposes. The proposed technique has the following features: (i) the entire image is taken into account while constructing the ridge feature map; (ii) minutiae matching is used to determine the translation and rotation parameters relating the query and the template images for ridge feature map extraction; (iii) filtering and ridge feature map extraction are implemented in the frequency domain thereby speeding up the matching process; (iv) filtered query images are catched to greatly increase the one-to-many matching speed. The hybrid matcher performs better than a minutiae-based fingerprint matching system. The genuine accept rate of the hybrid matcher is observed to be similar to10% higher than that of a minutiae-based system at low FAR values. Fingerprint verification (one-to-one matching) using the hybrid matcher on a Pentium III, 800 MHz system takes similar to1.4 s, while fingerprint identification (one-to-many matching) involving (000 templates takes similar to0.2 s per match. (C) 2003 Published by Elsevier Science Ltd on behalf of Pattern Recognition Society.
引用
收藏
页码:1661 / 1673
页数:13
相关论文
共 23 条
[1]  
[Anonymous], 1999, Biometrics: personal identification in networked society
[2]  
Bazen AM, 2000, P PRORISC2000 WORKSH
[3]  
Berry J, 2001, CRC FOR P S, P1
[4]  
Bhanu B, 2001, LECT NOTES COMPUT SC, V2091, P205
[5]   Fingerprint classification by directional image partitioning [J].
Cappelli, R ;
Lumini, A ;
Maio, D ;
Maltoni, D .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1999, 21 (05) :402-421
[6]  
Daugman J, 1999, Recognizing persons by their iris patterns, P103
[7]   UNCERTAINTY RELATION FOR RESOLUTION IN SPACE, SPATIAL-FREQUENCY, AND ORIENTATION OPTIMIZED BY TWO-DIMENSIONAL VISUAL CORTICAL FILTERS [J].
DAUGMAN, JG .
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 1985, 2 (07) :1160-1169
[8]  
Federal Bureau of Investigation, 1984, SCI FING CLASS US
[9]   Fingerprint matching using transformation parameter clustering [J].
Germain, RS ;
Califano, A ;
Colville, S .
IEEE COMPUTATIONAL SCIENCE & ENGINEERING, 1997, 4 (04) :42-49
[10]   Fingerprint image enhancement: Algorithm and performance evaluation [J].
Hong, L ;
Wan, YF ;
Jain, A .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1998, 20 (08) :777-789