Heterogenous Computing on Iris Matching with OpenCL

Abstract:

This study presents parallelization of Hamming Distance algorithm, which is used for iris comparison on iris recognition systems, for heterogeneous systems that can be included Central Processing Units (CPUs), Graphics Processing Units (GPUs), Digital Signal Processing (DSP) boards, Field Programmable Gate Array (FPGA) and some other mobile platforms with OpenCL. OpenCL allows to run same code on CPUs, GPUs, FPGAs and DSP boards. Heterogeneous computing refers to systems include different kind of devices (CPUs, GPUs, FPGAs and other accelerators). Heterogeneous computing gains performance or reduces power for suitable algorithms on these OpenCL supported devices. In this study, Hamming Distance algorithm has been coded with C++ as a sequential code and has been parallelized a designated method by us with OpenCL. Our OpenCL code has been executed on Nvidia GT430 GPU and Intel Xeon 5650 processor. The OpenCL code implementation demonstrates that speed up to 87 times with parallelization. Also our study differs from other studies, which accelerate iris matching, with regard to ensure heterogeneous computing by using OpenCL.

• Periodical: Applied Mechanics and Materials (Volume 850)
• Pages: 129-135
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.850.129
• Online since: August 2016
• Authors: Buğra Şimşek*, Nursel Akçam
• Keywords: CPUs, GPUs, Heterogeneous Computing, Heterogeneous Programming, Iris Matching, OpenCL, Parallel Computing, Parallel Programming

References

[1] Adler, F.H., Physiology of the Eye (Chapter VI, page 143), Mosby (1953).

[2] J. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 11, (1993).

DOI: 10.1109/34.244676

[3] J. Daugman. Biometric personal identification system based on iris analysis. United States Patent, Patent Number: 5, 291, 560, (1994).

[4] L. Masek  Recognition of human iris patterns for biometric identification, 2003 [online] Available: http: /www. csse. uwa. edu. au/~pk/studentprojects/libor.

[5] F.Z. Sakr, M. Taher, A.M. Wahba. High Performance Iris Recognition System On GPU, 2011 International Conference on Computer Engineering & Systems (ICCES), pp.237-242, (2011).

DOI: 10.1109/icces.2011.6141049

[6] Fatma Z. Sakr, M. Taher, A.M. Wahba. Accelerating Iris Recognition algorithms on GPUs, IEEE CIBEC, Cairo, Egypt, (2012).

DOI: 10.1109/cibec.2012.6473321

[7] Grabowski, K., Napieralski, A.: Hardware architecture optimized for Iris recognition, IEEE Trans. CSV T., 2011, 21, (9), p.1293–1303.

DOI: 10.1109/tcsvt.2011.2147150

[8] S. Sanderson, J. Erbetta. Authentication for secure environments based on iris scanning technology. IEE Colloquium on Visual Biometrics, (2000).

DOI: 10.1049/ic:20000468

[9] J. G. Daugman  How iris recognition works,  IEEE Trans. Circuits Syst. Video Technol.,  vol. 14,  no. 1,  pp.21-2004.

DOI: 10.1109/tcsvt.2003.818350

[10] H. Proenç, a and L.A. Alexandre, UBIRIS: A Noisy Iris Image Database, "Proc. 13th Int, l Conf. Image Analysis and Processing, pp.970-977, Sept. 2005, http: /iris. di. ubi. pt.

DOI: 10.1007/11553595_119

[11] Writing Optimal OpenCL™ Code with Intel® OpenCL SDK, 2010–2011 Intel Corporation, p.7.

[12] NVIDIA OpenCL Best Practices Guide Version 1. 0, 2009, p.1.

[13] Introduction to OpenCL Cliff Woolley, NVIDIA Developer Technology Group 2010, p.14.