Local relative location error descriptor-based fingerprint minutiae matching

Authors:
Xifeng Tong;Songbo Liu;Jianhua Huang;Xianglong Tang
Affiliations:
School of Computer Science and Technology, No. 352 Postal Box, Harbin Institute of Technology, No. 92, Xidazhi Street, Harbin 150001, PR China and Daqing Petroleum Institute, Daqing, China;School of Computer Science and Technology, No. 352 Postal Box, Harbin Institute of Technology, No. 92, Xidazhi Street, Harbin 150001, PR China;School of Computer Science and Technology, No. 352 Postal Box, Harbin Institute of Technology, No. 92, Xidazhi Street, Harbin 150001, PR China;School of Computer Science and Technology, No. 352 Postal Box, Harbin Institute of Technology, No. 92, Xidazhi Street, Harbin 150001, PR China
Venue:
Pattern Recognition Letters
Year:
2008

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Abstract

Minutiae matching with non-linear distortion is a challenging task in an Automatic Fingerprint Identification System. In this paper, a feature called Local Relative Location Error Descriptor (LRLED) is proposed to overcome non-linear distortion. The LRLED-based algorithm consists of three procedures. Firstly, a pairwise alignment method is proposed to achieve fingerprint alignment. Secondly, a matched minutiae-pair set is obtained with a comparatively loose threshold to reduce false non-matches, which lead not only to most of the corresponding minutiae-pairs, but also to a few non-corresponding minutiae-pairs getting matched. Finally, the LRLED-based similarity measure, which outputs a very high score for a corresponding minutiae-pair but a very low score for a non-corresponding minutiae-pair to reduce false matches, is employed to compute the similarity level between template and test fingerprints. Evaluations on FVC2002 and FVC2004 databases reveal that LRLED is good at distinguishing between corresponding and non-corresponding minutiae-pairs and works well for fingerprint minutiae matching.