Massively parallel palmprint identification system using GPU

  • Syed Ali Tariq
  • Shahzaib Iqbal
  • Mubeen Ghafoor
  • Imtiaz A. Taj
  • Noman M. Jafri
  • Saad Razzaq
  • Tehseen Zia
Article
First Online:
Received:
Revised:
Accepted:
  • 66 Downloads

Abstract

Automated human authentication is becoming increasingly important in today’s world due to increased need of security and surveillance applications deployed in almost all premises and installations. In this regard, palmprint biometric based identification has gained a lot of attention in recent years. However, due to large size of palmprint images and presence of principal lines, wrinkles, creases, and other noises, there are large number of inaccurate minutiae present. The computational requirement of palmprint identification is also quite large and it takes a lot of time to find identity of a palmprint in large database. In this study, a novel palmprint identification solution has been proposed that increases the accuracy of minutia detection based on improved frequency estimation and a novel region-quality based minutia extraction algorithm. Furthermore, a novel, efficient and highly accurate minutiae based encoding and matching algorithm is proposed that is designed to achieve maximum parallelism, and it is further accelerated using graphical processing unit. The results of the proposed palmprint identification demonstrate high accuracy and much faster identification speeds in comparison with current state of the art. Therefore, it can be considered as a robust, efficient and practical solution for palmprint based identification systems.

Keywords

Palmprint identification Minutia quality Parallel processing GPU CUDA 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, New York (2009)CrossRefMATHGoogle Scholar
  2. 2.
    Zheng, Q., Kumar, A., Pan, G.: Suspecting less and doing better: new insights on palmprint identification for faster and more accurate matching. IEEE Trans. Inf. Forensics Secur. 11(3), 633–41 (2016)CrossRefGoogle Scholar
  3. 3.
    Zhang, K., Huang, D., Zhang, D.: An optimized palmprint recognition approach based on image sharpness. Pattern Recognit. Lett. 85, 65–71 (2017)CrossRefGoogle Scholar
  4. 4.
    Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–89 (1998)CrossRefGoogle Scholar
  5. 5.
    Ghafoor, M., Taj, I.A., Jafri, M.N.: Fingerprint frequency normalisation and enhancement using two-dimensional short-time Fourier transform analysis. IET Comput. Vis. 10(8), 806–16 (2016)CrossRefGoogle Scholar
  6. 6.
    Kong, A., Zhang, D., Kamel, M.: A survey of palmprint recognition. Pattern Recognit. 42(7), 1408–18 (2009)CrossRefGoogle Scholar
  7. 7.
    Jain, A.K., Feng, J., Nagar, A., Nandakumar, K.: On matching latent fingerprints. In: Computer Vision and Pattern Recognition Workshops, 2008. In: CVPRW 2008. IEEE Computer Society Conference on 2008 Jun 23 (pp. 1–8). IEEE (2008)Google Scholar
  8. 8.
    Jain, A.K., Feng, J.: Latent palmprint matching. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 1032–47 (2009)CrossRefGoogle Scholar
  9. 9.
    Wang, R., Ramos, D., Veldhuis, R., Fierrez, J., Spreeuwers, L., Xu, H.: Regional fusion for high-resolution palmprint recognition using spectral minutiae representation. IET Biom. 3(2), 94–100 (2014)CrossRefGoogle Scholar
  10. 10.
    Chen, F., Huang, X., Zhou, J.: Hierarchical minutiae matching for fingerprint and palmprint identification. IEEE Trans. Image Process. 22(12), 4964–71 (2013)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Ghafoor, M., Taj, I.A., Ahmad, W., Jafri, M.N.: Efficient 2-fold contextual filtering approach for fingerprint enhancement. IET Image Process. 8(7), 417–25 (2014)CrossRefGoogle Scholar
  12. 12.
    Wang, W., Li, J., Huang, F., Feng, H.: Design and implementation of Log-Gabor filter in fingerprint image enhancement. Pattern Recognit. Lett. 29(3), 301–8 (2008)CrossRefGoogle Scholar
  13. 13.
    Chikkerur, S., Cartwright, A.N., Govindaraju, V.: K-plet and coupled BFS: a graph based fingerprint representation and matching algorithm. In: International Conference on Biometrics 2006 Jan 5 (pp. 309–315). Springer, Berlin (2006)Google Scholar
  14. 14.
    Jiang, X., Yau, W.Y.: Fingerprint minutiae matching based on the local and global structures. In: Pattern recognition. Proceedings. 15th International Conference on 2000 (Vol. 2, pp. 1038–1041). IEEE (2000)Google Scholar
  15. 15.
    Jea, T.Y., Govindaraju, V.: A minutia-based partial fingerprint recognition system. Pattern Recognit. 38(10), 1672–84 (2005)CrossRefGoogle Scholar
  16. 16.
    Duta, N., Jain, A.K., Mardia, K.V.: Matching of palmprints. Pattern Recognit. Lett. 23(4), 477–85 (2002)CrossRefMATHGoogle Scholar
  17. 17.
    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(12), 2128–41 (2010)CrossRefGoogle Scholar
  18. 18.
    Cappelli, R., Ferrara, M., Maio, D.: A fast and accurate palmprint recognition system based on minutiae. IEEE Trans. Syst. Man Cybern. Part B 42(3), 956–62 (2012)CrossRefGoogle Scholar
  19. 19.
    Dai, J., Zhou, J.: Multifeature-based high-resolution palmprint recognition. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 945–57 (2011)CrossRefGoogle Scholar
  20. 20.
    Dai, J., Feng, J., Zhou, J.: Robust and efficient ridge-based palmprint matching. IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1618–32 (2012)CrossRefGoogle Scholar
  21. 21.
    Rakvic, R.N., Ngo, H., Broussard, R.P., Ives, R.W.: Comparing an FPGA to a cell for an image processing application. EURASIP J. Adv. Signal Process. 2010(1), 764838 (2010)Google Scholar
  22. 22.
    Rakvic, R.N., Ulis, B.J., Broussard, R.P., Ives, R.W., Steiner, N.: Parallelizing iris recognition. IEEE Trans. Inf. Forensics Secur. 4(4), 812–23 (2009)CrossRefGoogle Scholar
  23. 23.
    Broussard, R.P., Rakvic, R.N., Ives, R.W.: Accelerating iris template matching using commodity video graphics adapters. In: Biometrics: Theory, Applications and Systems. BTAS 2008. 2nd IEEE International Conference on 2008 Sep 29 (pp. 1–6). IEEE (2008)Google Scholar
  24. 24.
    Nvidia, C.U.D.A.: Nvidia cuda c programming guide. Nvidia Corp. 120(18), 8 (2011)Google Scholar
  25. 25.
    Bolz, J., Farmer, I., Grinspun, E., Schröoder, P.: Sparse matrix solvers on the GPU: conjugate gradients and multigrid. In: ACM Transactions on Graphics (Vol. 22, No. 3, pp. 917–924). ACM (2011)Google Scholar
  26. 26.
    Krüger, J., Westermann, R.: Linear algebra operators for GPU implementation of numerical algorithms. In: ACM Transactions on Graphics (TOG) 2003 Jul 27 (Vol. 22, No. 3, pp. 908–916). ACM (2003)Google Scholar
  27. 27.
    Moreland, K., Angel, E.: The FFT on a GPU. In: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS Conference on Graphics Hardware 2003 Jul 26 (pp. 112–119). Eurographics Association (2003)Google Scholar
  28. 28.
    Wong, T.T., Leung, C.S., Heng, P.A., Wang, J.: Discrete wavelet transform on consumer-level graphics hardware. IEEE Trans. Multimed. 9(3), 668–73 (2007)CrossRefGoogle Scholar
  29. 29.
    Tenllado, C., Setoain, J., Prieto, M., Piñuel, L., Tirado, F.: Parallel implementation of the 2D discrete wavelet transform on graphics processing units: filter bank versus lifting. IEEE Trans. Parallel Distrib. Syst. 19(3), 299–310 (2008)CrossRefGoogle Scholar
  30. 30.
    Wong, T.T., Or, S.H., Fu, C.W.: Real-time relighting of compressed panoramas. In: Graphics Programming Methods 2003 Jan 1 (pp. 375–388). Charles River Media, Inc (2003)Google Scholar
  31. 31.
    Crookes, D., Boyle, K., Miller, P., Gillan, C.: GPU implementation of the affine transform for 3D image registration. In: Machine Vision and Image Processing Conference. IMVIP’09. 13th International 2009 Sep 2 (pp. 151–155). IEEE (2009)Google Scholar
  32. 32.
    Vandal, N.A., Savvides, M.: CUDA accelerated iris template matching on graphics processing units (GPUs). In: Biometrics: Theory Applications and Systems (BTAS). Fourth IEEE International Conference on 2010 Sep 27 (pp. 1–7). IEEE (2010)Google Scholar
  33. 33.
    Gajdoš, P., Platoš, J., Moravec, P.: Iris recognition on GPU with the usage of non-negative matrix factorization. In: Intelligent Systems Design and Applications (ISDA). 10th International Conference on 2010 Nov 29 (pp. 894–899). IEEE (2010)Google Scholar
  34. 34.
    Daugman, J.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)CrossRefGoogle Scholar
  35. 35.
    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)CrossRefGoogle Scholar
  36. 36.
    Ratha, N.K., Chen, S., Jain, A.K.: Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recognit. 28(11), 1657–72 (1995)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Syed Ali Tariq
    • 1
  • Shahzaib Iqbal
    • 2
  • Mubeen Ghafoor
    • 3
  • Imtiaz A. Taj
    • 4
  • Noman M. Jafri
    • 2
  • Saad Razzaq
    • 5
  • Tehseen Zia
    • 3
  1. 1.Department of Computing and TechnologyAbasyn UniversityIslamabadPakistan
  2. 2.Department of Electrical EngineeringAbasyn UniversityIslamabadPakistan
  3. 3.Department of Computer ScienceCOMSATS Institute of Information TechnologyIslamabadPakistan
  4. 4.Department of Electronics EngineeringCapital University of Science and TechnologyIslamabadPakistan
  5. 5.Department of Computer Science & ITUniversity of SargodhaSargodhaPakistan

Personalised recommendations