A Minutiae-Based GPU Acceleration Approach for Fingerprint Matching
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초록
In this paper, we propose a faster fingerprint matching and identification algorithm. The presented biometric technique is accelerated on GPU for better computational efficiency and faster matching process utilizing the parallel nature of the GPU processor. Our GPU-based features matching (FM) technique compares thousands of fingerprint images for recognition in a fraction of a second. We worked on GPU (NVIDIA GeForce GTX 260 with compute capability 1.3) and CPU（dual core-2-duo processor) for fingerprint recognition. Experimental results show that the computational efficiency of GPU-based recognition system is higher than the CPU-based implementation, and state-of-the-art methods implemented on CPU (MATLAB ANN), and ARM9 etc. The proposed features matching (FM) process for fingerprint recognition is validated on a large set of 2,000 images which are available online in FVC2000, FVC2002, and FVC2004 databases.
Abstract
In this paper, we propose a faster fingerprint matching and identification algorithm. The presented biometric technique is accelerated on GPU for better computational efficiency and faster matching process utilizing the parallel nature of the GPU processor. Our GPU-based features matching (FM) technique compares thousands of fingerprint images for recognition in a fraction of a second. We worked on GPU (NVIDIA GeForce GTX 260 with compute capability 1.3) and CPU（dual core-2-duo processor) for fingerprint recognition. Experimental results show that the computational efficiency of GPU-based recognition system is higher than the CPU-based implementation, and state-of-the-art methods implemented on CPU (MATLAB ANN), and ARM9 etc. The proposed features matching (FM) process for fingerprint recognition is validated on a large set of 2,000 images which are available online in FVC2000, FVC2002, and FVC2004 databases.
참고문헌 (17)
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