Automated fingerprint recognition using structural matching
Pattern Recognition
Fingerprint Image Enhancement: Algorithm and Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
A secure fingerprint matching technique
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Fingerprint Matching Algorithm Based on Tree Comparison using Ratios of Relational Distances
ARES '07 Proceedings of the The Second International Conference on Availability, Reliability and Security
A Minutiae-Based Fingerprint Matching Algorithm Using Phase Correlation
DICTA '07 Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications
Fingerprint Matching Using Global Minutiae and Invariant Moments
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04
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There are different methods and techniques used for matching fingerprints but the most common and popular approach is minutiae based matching. Our approach is based on structural matching and the matching algorithm presented here is the improved and modified form of [1]. In this method, matching is done on the basis of five closest neighbors of one single minutia that is also called a center minutia. An authentication of minutia is based on these surrounding neighbors. The approach we present here is divided in to two stages. First stage performs initial filtration and the second stage includes special matching criteria that incorporate fuzzy logic as well as a novel feature to select final minutiae for matching score calculation. The method of selecting center point for second stage is also adapted. This algorithm is able to perform well for translated, rotated and stretched fingerprints and does not require any process for alignment before matching. Experimental results show that algorithm is efficient and reliable.