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.
Similar content being viewed by others
References
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, New York (2009)
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)
Zhang, K., Huang, D., Zhang, D.: An optimized palmprint recognition approach based on image sharpness. Pattern Recognit. Lett. 85, 65–71 (2017)
Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–89 (1998)
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)
Kong, A., Zhang, D., Kamel, M.: A survey of palmprint recognition. Pattern Recognit. 42(7), 1408–18 (2009)
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)
Jain, A.K., Feng, J.: Latent palmprint matching. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 1032–47 (2009)
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)
Chen, F., Huang, X., Zhou, J.: Hierarchical minutiae matching for fingerprint and palmprint identification. IEEE Trans. Image Process. 22(12), 4964–71 (2013)
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)
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)
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)
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)
Jea, T.Y., Govindaraju, V.: A minutia-based partial fingerprint recognition system. Pattern Recognit. 38(10), 1672–84 (2005)
Duta, N., Jain, A.K., Mardia, K.V.: Matching of palmprints. Pattern Recognit. Lett. 23(4), 477–85 (2002)
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)
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)
Dai, J., Zhou, J.: Multifeature-based high-resolution palmprint recognition. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 945–57 (2011)
Dai, J., Feng, J., Zhou, J.: Robust and efficient ridge-based palmprint matching. IEEE Trans. Pattern Anal. Mach. Intell. 34(8), 1618–32 (2012)
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)
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)
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)
Nvidia, C.U.D.A.: Nvidia cuda c programming guide. Nvidia Corp. 120(18), 8 (2011)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Daugman, J.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)
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)
Ratha, N.K., Chen, S., Jain, A.K.: Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recognit. 28(11), 1657–72 (1995)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tariq, S.A., Iqbal, S., Ghafoor, M. et al. Massively parallel palmprint identification system using GPU. Cluster Comput 22 (Suppl 3), 7201–7216 (2019). https://doi.org/10.1007/s10586-017-1121-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-017-1121-z