Abstract
Fingerprints are the most used biometrics features for identification. Although state-of-the-art algorithms are very accurate, but the need for fast processing speed for databases containing millions fingerprints is highly demanding. GPU devices are widely used in parallel computing tasks for its efficiency and low-cost. In this paper, we propose to adapt minutia cylinder-code (MCC) matching algorithm, an efficient algorithm in term of accuracy to GPU. The proposed method fits well with the architecture of the GPU that makes it easy to implement. The results of our experiments with a GTX- 680 device show that the proposed algorithm can perform 8.5 millions matches in a second that is suitable for real time identification systems having databases containing millions of fingerprints.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, London (2009)
Cappelli, R., Maio, D., Maltoni, D., Wayman, J.L., Jain, A.K.: Performance evaluation of fingerprint verification systems. IEEE Trans. Pattern Anal. Mach. Intell. 28, 3–18 (2006)
Chikkerur, S., Cartwright, A.N., Govindaraju, V.: K-plet and Coupled BFS: a graph based fingerprint representation and matching algorithm. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 309–315. Springer, Heidelberg (2005)
Cappelli, R., Ferrara, M., Maltoni, D.: Minutia cylinder-code: A new representation and matching technique for fingerprint recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32, 2128–2141 (2010)
Xu, W., Chen, X., Feng, J.: A Robust Fingerprint Matching Approach: Growing and Fusing of Local Structures. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 134–143. Springer, Heidelberg (2007)
Feng, J.: Combining minutiae descriptors for fingerprint matching. Pattern Recogn. 41, 342–352 (2008)
Wang, X., Li, J., Niu, Y.: Fingerprint matching using orientation codes and polylines. Pattern Recogn. 40, 3164–3177 (2007)
Feng, J., Ouyang, Z., Cai, A.: Fingerprint matching using ridges. Pattern Recogn. 39, 2131–2140 (2006)
Qi, J., Yang, S., Wang, Y.: Fingerprint matching combining the global orientation field with minutia. Pattern Recogn. Lett. 26, 2424–2430 (2005)
Tico, M., Kuosmanen, P.: Fingerprint matching using an orientation-based minutia descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 25, 1009–1014 (2003)
Medina-Pérez, M.A., GarcÃa-Borroto, M., Gutierrez-Rodriguez, A.E., Altamirano-Robles, L.: Robust fingerprint verification using m-triplets. In: International Conference on Hand-Based Biometrics (ICHB 2011), Hong Kong, pp. 1–5 (2011)
Gutierrez, P.D., Lastra, M., Herrera, F., Benitez, J.M.: A high performance fingerprint matching system for large databases based on GPU. IEEE Trans. Inf. Forensics Secur. 9(1), 62–71 (2014)
Cappelli, R., Ferrara, M., Maltoni, D.: Large-scale fingerprint identification on GPU. Inf. Sci. 306, 1–20 (2015)
Peralta, D., Triguero, I., Sanchez-Reillo, R., Herrera, F., Benitez, J.M.: Fast fingerprint identification for large databases. Pattern Recogn. 47(2), 588–602 (2014)
Luebke, D., et al.: GPGPU: general-purpose computation on graphics hardware. In: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, SC 2006 (2006)
Cappelli, R., Maio, D.: State-of-the-art in fingerprint classification. In: Ratha, N., Bolle, R. (eds.) Automatic Fingerprint Recognition Systems, pp. 183–205. Springer, New York (2004)
Hong, J.H., Min, J.K., Cho, U.K., Cho, S.B.: Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers. Pattern Recogn. 41(2), 662–671 (2008)
Cappelli, R., Ferrara, M., Maltoni, D.: Fingerprint indexing based on minutia cylinder code. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 1051–1057 (2010)
Bhanu, B., Tan, X.: A triplet based approach for indexing of fingerprint database for identification. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 205–210. Springer, Heidelberg (2001)
Unique Identification Authority of India, Role of Biometric Technology in Aadhaar Enrollment (2012)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS 2012, pp. 1106–1114 (2012)
Zhang, Y., Yi, D., Wei, B., Zhuang, Y.: A GPU-accelerated non-negative sparse latent semantic analysis algorithm for social tagging data. Inform. Sci. 281, 687–702 (2014)
Friedrichs, M., Eastman, P., Vaidyanathan, V., Houston, M., Legrand, S., Beberg, A., et al.: Accelerating molecular dynamic simulation on graphics processing units. J. Comput. Chem. 30(6), 864–872 (2009)
Schatz, M., Trapnell, C., Delcher, A., Varshney, A.: High-throughput sequence alignment using graphics processing units. BMC Bioinformat. 8, 474 (2007)
Medina-Pérez, M.A., Loyola-González, O., Gutierrez-RodrÃguez, A.E., GarcÃa-Borroto, M., Altamirano-Robles, L.: Introducing an experimental framework in C# for fingerprint recognition. In: MartÃnez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-Lopez, J.A., Salas-RodrÃguez, J., Suen, C.Y. (eds.) MCPR 2014. LNCS, vol. 8495, pp. 132–141. Springer, Heidelberg (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Le, H.H., Nguyen, N.H., Nguyen, T.T. (2016). Exploiting GPU for Large Scale Fingerprint Identification. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_66
Download citation
DOI: https://doi.org/10.1007/978-3-662-49381-6_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-49380-9
Online ISBN: 978-3-662-49381-6
eBook Packages: Computer ScienceComputer Science (R0)