Efficient fingerprint matching using GPU

• Published in: IET image processing 2018-02, Vol.12 (2), p.274-284
• Main Authors: Ghafoor, Mubeen, Iqbal, Shahzaib, Tariq, Syed Ali, Taj, Imtiaz A, Jafri, Noman M
• Format: Article
• Language: English
• Subjects: automated FI system; computationally intensive algorithms; data structures; fingerprint identification; fingerprint matching algorithm; GPU technology; graphical processing unit; graphics processing units; image coding; image matching; image sequences; loop unrolling; minutia neighbour‐based novel encoding algorithm; multithreading; multi‐threading; nested loop minimisation; open source matcher; parallel computational power; Research Article; sequential central processing
• ISSN: 1751-9659; 1751-9667; 1751-9667
• DOI: 10.1049/iet-ipr.2016.1021

Graphical processing unit (GPU) has proven a beneficial tool in handling computationally intensive algorithms, by introducing massive parallelism in the calculations. In this study, an effective and low-cost fingerprint identification (FI) solution is proposed that can exploit the parallel computational power of GPU proficiently. It is achieved by mapping a generalised minutia neighbour-based novel encoding and matching algorithm on low-cost GPU technology. The proposed solution achieves high accuracy in comparison with two open source matchers and it is shown to be scalable by comparing matching performance on different GPUs. The proposed GPU implementation employs multithreading and loop unrolling, which minimises the use of nested loops and avoids sequential matching of encoded minutia features. After a thorough and careful designing of data structures, memory transfers and computations, a GPU-based fingerprint matching system is developed. It achieves on average 50,196 fingerprint matches per second on a single GPU. As compared to the sequential central processing unit implementation, the proposed system achieves a speed up of around 92 times, while maintaining the accuracy. The proposed system with matcher integrated on GPU can be considered as a good, low-cost, robust and efficient solution for large-scale applications of automated FI systems.