A novel fingerprint matching and classification schema based on nested convex polygons

Authors:
Hamzeh Khazaei;Ali Mohades
Affiliations:
Mathematics and Computer Science Department, Amirkabir University of Technology, Tehran, Iran;Mathematics and Computer Science Department, Amirkabir University of Technology, Tehran, Iran
Venue:
MATH'08 Proceedings of the American Conference on Applied Mathematics
Year:
2008

Citing 6
Cited 0

On-Line Fingerprint Verification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of computational geometry

Handbook of computational geometry
Handbook of Fingerprint Recognition

Handbook of Fingerprint Recognition
A Fingerprint Matching Algorithm Based On Delaunay Triangulation Net

CIT '05 Proceedings of the The Fifth International Conference on Computer and Information Technology
Delaunay Triangulation Algorithm for Fingerprint Matching

ISVD '06 Proceedings of the 3rd International Symposium on Voronoi Diagrams in Science and Engineering
Minutiae matching based fingerprint verification using delaunay triangulation and aligned-edge-guided triangle matching

AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a new approach to fingerprint matching and classification using an onion layer algorithm of computational geometry. In order to extract valid minutiae we apply some image processing steps on input fingerprint. Using an Onion layer algorithm we construct nested convex polygons of minutiae, then based on polygons property, we perform matching and classification of fingerprints; we use the most interior polygon in order to calculate the rigid transformations parameters and perform local matching, consequently, global matching applied. This method rejects non matching fingerprint in local matching and avoid time consuming global matching steps. We develop new classification scheme of fingerprints based on this approach. Unlike classic classification of fingerprints, this novel approach distributed fingerprints in classes equally, and none of image processing techniques are required for this classification. This normal distribution of fingerprints in different classes has great effect on identification time.