Direct Pore Matching for Fingerprint Recognition

Abstract

Abstract. Sweat pores on fingerprints have proven to be useful features for personal identification. Several methods have been proposed for pore matching. The state-of-the-art method first matches minutiae on the fingerprints and then matches the pores based on the minutia matching results. A problem of such minutia-based pore matching method is that the pore matching is dependent on the minutia matching. Such dependency limits the pore matching performance and impairs the effectiveness of the fusion of minutia and pore match scores. In this paper, we propose a novel direct approach for matching fingerprint pores. It first determines the correspondences between pores based on their local features. It then uses the RANSAC (RANdom SAmple Consensus) algorithm to refine the pore correspondences obtained in the first step. A similarity score is finally calculated based on the pore matching results. The proposed pore matching method successfully avoids the dependency of pore matching on minutia matching results. Experiments have shown that the fingerprint recognition accuracy can be greatly improved by using the method proposed in this paper.

Fingerprint Pore Matching Based on Sparse Representation

“…Here, we will use the same local descriptors as in [7] for pores so that we can fairly compare the performance of SR-based and correlationbased approaches. The local descriptor N R p ∈ of a pore as defined in [7] essentially captures the intensity variation in a circular neighborhood to the pore. To calculate the differences between pores, we will use the SR-based rather than correlation-based technique.…”

“…The goal of this refinement step is thus to eliminate these false pore correspondences from the obtained coarse pore correspondences. The difference between the method we use here and the method in [7] is that when applying RANSAC, we choose pore correspondences according to the differences between the pores in the correspondences such that the pore correspondences with smaller differences will be chosen with higher probability than those with larger differences. That is we employ the WRANSAC algorithm [8] to refine pore correspondences.…”

“…The first database, denoted as DBI, is the same database as the one used in [7], which contains 1,480 fingerprint images from 148 fingers (five images collected for each finger in each of two sessions separated by about two weeks). The images in DBI have a spatial size of 320 pixels by 240 pixels.…”

“…They have been proven to be very useful for improving the accuracy of existing minutiaebased AFRS [2][3][4][5][6][7]. In this paper, we will focus on the matching of fingerprint pores.…”

“…Methods in the first category base the pore matching on the alignment estimated by using other features such as minutiae [2,[4][5][6], whereas those in the second category directly pair pores on fingerprints based on their local features and then refine the pore correspondences based on certain global transformation model [7]. Compared with the first kind of methods, the direct pore matching method has been shown to be more effective [7].…”