An adaptive hybrid energy-based fingerprint matching technique
In this paper we present a new adaptive hybrid energy-based fingerprint matching system, which combines both minutiae information available in a fingerprint with the information of the local ridges in their vicinity. A more continuous representation of fingerprints can be obtained by using an energy-based rectangular tessellation with non-overlapped squared cells. However, a fixed tessellation is not efficient in handling non-linear deformations in fingerprints for which we propose an adaptive matching technique that uses dynamic rectangular tessellation to handle them. Each time a match is not found the dynamic tessellation increases its cell size until there is a match or cell size is greater than image size where the fingerprint is rejected. The basic idea of this system is to divide the fingerprint-matching problem into several small sub-problems that involve the use of cell energy minimization for which an iterative schema is devised. At each minimization step this schema optimizes its local energy according to the previous estimate and the observed image features. Minutiae and local ridges in their vicinity, produce different amounts of energy which form the energy vectors of the fingerprint image. In this work, we focus on the difficult problem of recognizing known fingerprints while rejecting unknown ones. Our system was tested on FVC2000 benchmark database of fingerprints and showed promising results. We show that matching performance can be improved by using energy vectors and adaptive matching, where adaptive matching reduces the effect of intra-class variations between different impressions of the same fingerprint image and energy vectors can efficiently represent fingerprints by using both information extracted from the minutiae and their local surrounding ridges.
fingerprints adaptive matching energy minimization minutiae matching proportion ranks