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DFIS: A Scalable Distributed Fingerprint Identification System

  • Yunxiang Zhao
  • Wanxin Zhang
  • Dongsheng Li
  • Zhen Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9530)

Abstract

Fingerprint has been widely used in a variety of biometric identification systems. However, with the rapid development of fingerprint identification systems, the amount of fingerprints information stored in systems has been rising sharply, making it challenging to process and store fingerprints efficiently and robustly with traditional stand-alone systems and relational databases. In this paper, we propose a scalable distributed fingerprint identification system, named DFIS. It combines the feature extraction procedure with HIPI library and optimizes the load balance strategy of MongoDB to construct a much more robust and stable system. Related experiments and simulations have been carried out and results show that DFIS can reduce the time expense by \(70\,\%\) during the features extraction procedural. For load balance of MongoDB, DFIS can decrease the difference of access load to less than \(5\,\%\) and meanwhile decrease \(50\,\%\) data migration to gain more reasonable distribution of operation load and data load among shards compared with the default load balance strategy in MongoDB.

Keywords

Fingerprint identification Distributed computing HIPI MongoDB 

Notes

Acknowledgments

This work is supported by the National Basic Research Program of China (973) under Grant No.2014CB340303, the National Natural Science Foundation of China under Grant No.61222205, No.61402490, and No.61303064. This work is also supported by the Program for New Century Excellent Talents in University, the Fok Ying-Tong Education Foundation under Grant No. 141066, and Foundation of Distinguished PHD Thesis of Hunan Province.

References

  1. 1.
    Chodorow, K.: Scaling MongoDB. O’Reilly Media Inc, Sebastopol (2011)Google Scholar
  2. 2.
    Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  3. 3.
    Engines, D.: Db-engines ranking (2013)Google Scholar
  4. 4.
    Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)CrossRefGoogle Scholar
  5. 5.
    Indrawan, G., Sitohang, B., Akbar, S.: Parallel processing for fingerprint feature extraction. In: 2011 International Conference on Electrical Engineering and Informatics (ICEEI), pp. 1–6. IEEE (2011)Google Scholar
  6. 6.
    Khanyile, N., Tapamo, J., Dube, E.: Distributed fingerprint enhancement on a multicore cluster (2012)Google Scholar
  7. 7.
    Lastra, M., Carabaño, J., Gutiérrez, P.D., Benítez, J.M., Herrera, F.: Fast fingerprint identification using gpus. Inf. Sci. 301, 195–214 (2015)CrossRefGoogle Scholar
  8. 8.
    Mader, K., Donahue, L.R., Müller, R., Stampanoni, M.: High-throughput, scalable, quantitative, cellular phenotyping using x-ray tomographic microscopy (2014)Google Scholar
  9. 9.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of fingerprint recognition. Springer Science and Business Media, London (2009)Google Scholar
  10. 10.
    Membrey, P., Plugge, E., Hawkins, D.: The Definitive Guide to MongoDB: The noSQL Database for Cloud and Desktop Computing. Apress, Beijing (2010)Google Scholar
  11. 11.
    Sweeney, C., Liu, L., Arietta, S., Lawrence, J.: Hipi: A Hadoop Image Processing Interface for Image-based Mapreduce Tasks. University of Virginia, Chris (2011)Google Scholar
  12. 12.
    White, T.: Hadoop: The Definitive Guide. O’Reilly Media Inc, Sebastopol (2012)Google Scholar
  13. 13.
    Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, vol. 10, p. 10 (2010)Google Scholar
  14. 14.
    Zhu, E., Hancock, E., Yin, J., Zhang, J., An, H.: Fusion of multiple candidate orientations in fingerprints. In: Kamel, M., Campilho, A. (eds.) ICIAR 2011, Part II. LNCS, vol. 6754, pp. 89–100. Springer, Heidelberg (2011) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yunxiang Zhao
    • 1
  • Wanxin Zhang
    • 1
  • Dongsheng Li
    • 1
  • Zhen Huang
    • 1
  1. 1.National Laboratory for Parallel and Distributed Processing College of ComputerNational University of Defence TechnologyChangshaPeople’s Republic of China

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