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.
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References
Cappelli, R., Ferrara, M., Franco, A., et al., 2007. Fingerprint verification competition 2006. Biomet. Technol. Today, 15(7–8):7–9. http://dx.doi.org/10.1016/s0969-4765(07)70140-6
Dede, E., Govindaraju, M., Gunter, D., et al., 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
Galar, M., Derrac, J., Peralta, D., et al., 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
Galar, M., Derrac, J., Peralta, D., et al., 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
Gutiérrez, P.D., Lastra, M., Herrera, F., et al., 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
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
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
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
Lastra, M., Carabaño, J., Gutiérrez, P., et al., 2015. Fast fingerprint identification using GPUs. Inform. Sci., 301:195–214. http://dx.doi.org/10.1016/j.ins.2014.12.052
Li, J., Li, D., Ye, Y., et al., 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
Liu, C., Ouyang, K., Chu, X., et al., 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
Mader, K., Donahue, L., Müller, R., et al., 2014. Highthroughput, scalable, quantitative, cellular phenotyping using X-ray tomographic microscopy. Proc. 2nd Int. Work-Conf. on Bioinformatics and Biomedical Engineering, p.1483–1498.
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
Peralta, D., Triguero, I., Sanchez-Reillo, R., et al., 2014. Fast fingerprint identification for large databases. Patt. Recog., 47(2):588–602. http://dx.doi.org/10.1016/j.patcog.2013.08.002
Peralta, D., Galar, M., Triguero, I., et al., 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
Plugge, E., Hawkins, D., Membrey, P., 2010. The Definitive Guide to MongoDB: the NoSQL Database for Cloud and Desktop Computing. Apress.
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
Sweeney, C., Liu, L., Arietta, S., et al., 2011. HIPI: a Hadoop Image Processing Interface for Image-Based MapReduce Tasks. MS Thesis, University of Virginia, USA.
Xu, J., Jiang, J., Dou, Y., et al., 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
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
Zhao, Y., Zhang, W., Li, D., et al., 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
Zhu, E., Yin, J., Zhang, G., 2004. Computation of fingerprint inter-ridge distance. J. Microelectron. Comput., 21(10):7–9 (in Chinese).
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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Project supported by the National Basic Research Program (973) of China (No. 2014CB340303), the National Natural Science Foundation of China (Nos. 61222205 and 61402490), the Program for New Century Excellent Talents in University, China (No. 141066), and the Fok Ying-Tong Education Foundation
A preliminary version was presented at the 15th International Conference on Algorithms and Architectures for Parallel Processing, Zhangjiajie, China, Nov. 18–20, 2015
ORCID: Dong-sheng LI, http://orcid.org/0000-0001-9743-2034
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Zhao, Yx., Zhang, Wx., Li, Ds. et al. Pegasus: a distributed and load-balancing fingerprint identification system. Frontiers Inf Technol Electronic Eng 17, 766–780 (2016). https://doi.org/10.1631/FITEE.1500487
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DOI: https://doi.org/10.1631/FITEE.1500487