Fingerprint identification using graph matching
Pattern Recognition
Primitives for the manipulation of general subdivisions and the computation of Voronoi
ACM Transactions on Graphics (TOG)
A Fingerprint Verification System Based on Triangular Matching and Dynamic Time Warping
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Identification Using Delaunay Triangulation
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
A Novel Fingerprint Matching Scheme Based on Local Structure Compatibility
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
K-plet and coupled BFS: a graph based fingerprint representation and matching algorithm
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Assessing the role of spatial relations for the object recognition task
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Hybrid algorithm for fingerprint matching using delaunay triangulation and local binary patterns
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Multimedia Tools and Applications
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In this paper, we represent a fingerprint image with a Delaunay graph formed by minutiae as nodes. The graph has attributes which contributes to the final similarity measure and are invariant under rotation and translation. We design an algorithm for the comparison of these graphs based on the similarities of multiple common substructures. We use different heuristics to tackle the problems of noise, deformation and partial matching found in fingerprint recognition. We match star structures and extend it by edges maintaining the local structural compatibility. Finally, we consolidate the global similarity taking into account the size of the common substructures and the accumulated similarity of all stars involved. We use a simple greedy algorithm obtaining a very efficient performance. We use our proposed method in some experiments with fingerprint images in databases from FVC2002. It shows better results compared to other known algorithms as K-plet, and several others recently published.