A High Performance Fingerprint Matching System for Large Databases Based on GPU

Fingerprints are the biometric features most used for identification. They can be characterized through some particular elements called minutiae. The identification of a given fingerprint requires the matching of its minutiae against the minutiae of other fingerprints. Hence, fingerprint matching is a key process. The efficiency of current matching algorithms does not allow their use in large fingerprint databases; to apply them, a breakthrough in running performance is necessary. Nowadays, the minutia cylinder-code (MCC) is the best performing algorithm in terms of accuracy. However, a weak point of this algorithm is its computational requirements. In this paper, we present a GPU fingerprint matching system based on MCC. The many-core computing framework provided by CUDA on NVIDIA Tesla and GeForce hardware platforms offers an opportunity to enhance fingerprint matching. Through a thorough and careful data structure, computation and memory transfer design, we have developed a system that keeps its accuracy and reaches a speed-up up to $100.8\\times$ compared with a reference sequential CPU implementation. A rigorous empirical study over captured and synthetic fingerprint databases shows the efficiency of our proposal. These results open up a whole new field of possibilities for reliable real time fingerprint identification in large databases.