Abstract
ABSTRACTFingerprint identification is one of the most common biometric feature problems which is used in many applications. Although state-of-the-art algorithms are very accurate, the need for fast processing a big database of millions of fingerprints is highly demanding. In this paper, we propose to adapt the fingerprint matching algorithm based on Minutia Cylinder-Code on Graphics Processing Units to speed up the matching. Another contribution of this paper is to add a consolidation stage after matching to enhance the precision. The experimental results on a GTX-680 and K40 tesla devices with standard data-sets prove that the proposed algorithm can be comparable with the state-of-the-art approach, and is suitable for a real-time identification application.
Highlights
Fingerprint matching is the task of estimating the degree of similarity between two given fingerprint images
We propose to adapt the fingerprint matching algorithm based on Minutia Cylinder-Code on Graphics Processing Units to speed up the matching
Each cylinder is divided into Ns × Ns × Nd cells as shown in Figure 2, where Ns is the resolution of the discretized 2D space around minutia m (Ns × Ns) and Nd is the height of the cylinder (i.e. 2π)
Summary
Fingerprint matching is the task of estimating the degree of similarity between two given fingerprint images (hereafter called fingerprint, for short). Graphic cards with general purpose graphics processing units (GPGPUs, hereafter called GPU for short) are a new parallel architecture which have been proven to be very useful for accelerating the processing speed of computationally intensive algorithms These devices include thousands of processing units; they provide massive parallel calculations and have been applied successfully in many fields such as artificial intelligence (Krizhevsky, Sutskever, & Hinton, 2012; Zhang, Yi, Wei, & Zhuang, 2014), simulation (Friedrichs et al, 2009), bioinformatics (Schatz, Trapnell, Delcher, & Varshney, 2007), as well as fingerprint matching (Cappelli et al, 2015; Gutierrez et al, 2014).
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