Pegasus: a distributed and load-balancing fingerprint identification system

Abstract

Fingerprint has been widely used in a variety of biometric identification systems in the past several years due to its uniqueness and immutability. With the rapid development of fingerprint identification techniques, many fingerprint identification systems are in urgent need to deal with large-scale fingerprint storage and high concurrent recognition queries, which bring huge challenges to the system. In this circumstance, we design and implement a distributed and load-balancing fingerprint identification system named Pegasus, which includes a distributed feature extraction subsystem and a distributed feature storage subsystem. The feature extraction procedure combines the Hadoop Image Processing Interface (HIPI) library to enhance its overall processing speed; the feature storage subsystem optimizes MongoDB’s default load balance strategy to improve the efficiency and robustness of Pegasus. Experiments and simulations are carried out, and results show that Pegasus can reduce the time cost by 70% during the feature extraction procedure. Pegasus also balances the difference of access load among front-end mongos nodes to less than 5%. Additionally, Pegasus reduces over 40% of data migration among back-end data shards to obtain a more reasonable data distribution based on the operation load (insertion, deletion, update, and query) of each shard.

Keywords

Distributed fingerprint identification / Distributed MongoDB / Load balancing

References

[1] Cappelli, R., Ferrara, M., Franco, A., , 2007. Fingerprint verification competition 2006. Biomet. Technol. Today, 15(7-8):7–9. http://dx.doi.org/10.1016/s0969-4765(07)70140-6

[2] Dede, E., Govindaraju, M., Gunter, D., , 2013. Performance evaluation of a MongoDB and Hadoop platform for scientific data analysis. Proc. 4th ACM Workshop on Scientific Cloud Computing, p.13–20. http://dx.doi.org/10.1145/2465848.2465849

[3] Galar, M., Derrac, J., Peralta, D., , 2015a. A survey of fingerprint classification part I: taxonomies on feature extraction methods and learning models. Knowl.-Based Syst., 81:76–97. http://dx.doi.org/10.1016/j.knosys.2015.02.008

[4] Galar, M., Derrac, J., Peralta, D., , 2015b. A survey of fingerprint classification part II: experimental analysis and ensemble proposal. Knowl.-Based Syst., 81:98–116. http://dx.doi.org/10.1016/j.knosys.2015.02.015

[5] Gutiérrez, P.D., Lastra, M., Herrera, F., , 2014. A high performance fingerprint matching system for large databases based on GPU. IEEE Trans. Inform. Forens. Secur., 9(1):62–71. http://dx.doi.org/10.1109/tifs.2013.2291220

[6] Hong, L., Wan, Y., Jain, A., 1998. Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Patt. Anal. Mach. Intell., 20(8):777–789. http://dx.doi.org/10.1109/34.709565

[7] Indrawan, G., Sitohang, B., Akbar, S., 2011. Parallel processing for fingerprint feature extraction. Proc. Int. Conf. on Electrical Engineering and Informatics, p.1–6. http://dx.doi.org/10.1109/iceei.2011.6021606

[8] Kanoje, S., Powar, V., Mukhopadhyay, D., 2015. Using MongoDB for social networking website deciphering the pros and cons. Proc. Int. Conf. on Innovations in Information, Embedded and Communication Systems, p.1–3. http://dx.doi.org/10.1109/iciiecs.2015.7192924

[9] Lastra, M., Carabaño, J., Gutiérrez, P., , 2015. Fast fingerprint identification using GPUs. Inform. Sci., 301:195–214. http://dx.doi.org/10.1016/j.ins.2014.12.052

[10] Li, J., Li, D., Ye, Y., , 2015. Efficient multi-tenant virtual machine allocation in cloud data centers. Tsinghua Sci. Technol., 20(1):81–89. http://dx.doi.org/10.1109/tst.2015.7040517

[11] Liu, C., Ouyang, K., Chu, X., , 2015. R-memcached: a reliable in-memory cache for big key-value stores. Tsinghua Sci. Technol., 20(6):560–573. http://dx.doi.org/10.1109/tst.2015.7349928

[12] Mader, K., Donahue, L., Müller, R., , 2014. Highthroughput, scalable, quantitative, cellular phenotyping using X-ray tomographic microscopy. Proc. 2nd Int. Work-Conf. on Bioinformatics and Biomedical Engineering, p.1483–1498.

[13] Malakar, R., Vydyanathan, N., 2013. A CUDA-enabled Hadoop cluster for fast distributed image processing. Proc. National Conf. on Parallel Computing Technologies, p.1–5. http://dx.doi.org/10.1109/parcomptech.2013.6621392

[14] Peralta, D., Triguero, I., Sanchez-Reillo, R., , 2014. Fast fingerprint identification for large databases. Patt. Recog., 47(2):588–602. http://dx.doi.org/10.1016/j.patcog.2013.08.002

[15] Peralta, D., Galar, M., Triguero, I., , 2015. A survey on fingerprint minutiae-based local matching for verification and identification: taxonomy and experimental evaluation. Inform. Sci., 315:67–87. http://dx.doi.org/10.1016/j.ins.2015.04.013

[16] Plugge, E., Hawkins, D., Membrey, P., 2010. The Definitive Guide to MongoDB: the NoSQL Database for Cloud and Desktop Computing. Apress.

[17] Shu, Y., Gu, Y.J., Chen, J., 2014. Dynamic authentication with sensory information for the access control systems. IEEE Trans. Parall. Distr. Syst., 25(2):427–436. http://dx.doi.org/10.1109/TPDS.2013.153

[18] Sweeney, C., Liu, L., Arietta, S., , 2011. HIPI: a Hadoop Image Processing Interface for Image-Based MapReduce Tasks. MS Thesis, University of Virginia, USA.

[19] Xu, J., Jiang, J., Dou, Y., , 2014. A low-cost fully pipelined architecture for fingerprint matching. Proc. 12th Int. Conf. on Signal Processing, p.413–418. http://dx.doi.org/10.1109/icosp.2014.7015039

[20] Zhang, Z., Li, D., Wu, K., 2016. Large-scale virtual machines provisioning in clouds: challenges and approaches. Front. Comput. Sci., 10(1):2–18. http://dx.doi.org/10.1007/s11704-015-4420-7

[21] Zhao, Y., Zhang, W., Li, D., , 2015. DFIS: a scalable distributed fingerprint identification system. Proc. 15th Int. Conf. on Algorithms and Architectures for Parallel Processing, p.162–175. http://dx.doi.org/10.1007/978-3-319-27137-8_13

[22] Zhu, E., Yin, J., Zhang, G., 2004. Computation of fingerprint inter-ridge distance. J. Microelectron. Comput., 21(10):7–9 (in Chinese).

[23] Zhu, E., Yin, J., Zhang, G., 2005. Fingerprint matching based on global alignment of multiple reference minutiae. Patt. Recog., 38(10):1685–1694. http://dx.doi.org/10.1016/j.patcog.2005.02.016

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2016 Zhejiang University and Springer-Verlag Berlin Heidelberg