Abstract
Today, fingerprint identification is the most common method of biometric identification. Existing fingerprint identification models have some defects that reduce the speed and quality of identification. So most of the models do not take into account the topological characteristics of images, for example, the classical method of measuring the ridge count value may produce incorrect results in areas of significant curvature of the ridge lines. This paper presents a new mathematical model for fingerprint identification, taking into account their topological characteristics. Identification is performed on the basis of templates. The templates contain a list of minutiae detected on the image and a list of ridge lines. For the ridge lines and minutiae, sets of topological vectors are constructed. The result of building topological vectors does not depend on the location of minutiae and takes into account their possible mutations, which increases the stability of the proposed mathematical model. Additionally, the stability of the model is ensured by combining the base topological vectors constructed for all minutiae and ridge lines into an expanded topological vector. This view allows you to significantly reduce the size of the template and optimize the use of memory. To compare the fingerprints the Delaunay triangulation is used based on the list of constructed topological vectors. 112 possible classes for topological vectors are defined. This approach allows you to increase the speed of identification up to 10 times while maintaining its accuracy. The proposed classification is resistant to rotation and displacement of images.
Highlights
Fingerprint images (FI) themselves are not used to identify fingerprints, but templates created on the basis of them using mathematical functions
The paper presents a mathematical model for the FI identification using topological vectors for the ridge lines
Possible mutations of the minutiae do not violate the numbering of links and the order of consideration of the minutiae
Summary
Fingerprint identification is the most common method of biometric identification. The result of building topological vectors does not depend on the location of minutiae and takes into account their possible mutations, which increases the stability of the proposed mathematical model. The stability of the model is ensured by combining the base topological vectors constructed for all minutiae and ridge lines into an expanded topological vector. This view allows you to significantly reduce the size of the template and optimize the use of memory. 112 possible classes for topological vectors are defined This approach allows you to increase the speed of identification up to 10 times while maintaining its accuracy.
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