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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2014 - 1
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Examination of Speed Contribution of Parallelization for Several Fingerprint Pre-Processing Algorithms

GORGUNOGLU, S. See more information about GORGUNOGLU, S. on SCOPUS See more information about GORGUNOGLU, S. on IEEExplore See more information about GORGUNOGLU, S. on Web of Science, ORAK, I. M. See more information about  ORAK, I. M. on SCOPUS See more information about  ORAK, I. M. on SCOPUS See more information about ORAK, I. M. on Web of Science, CAVUSOGLU, A. See more information about  CAVUSOGLU, A. on SCOPUS See more information about  CAVUSOGLU, A. on SCOPUS See more information about CAVUSOGLU, A. on Web of Science, GOK, M. See more information about GOK, M. on SCOPUS See more information about GOK, M. on SCOPUS See more information about GOK, M. on Web of Science
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Download PDF pdficon (977 KB) | Citation | Downloads: 824 | Views: 2,662

Author keywords
CUDA, fingerprint recognition, parallel processing, parallel programming, OpenMP

References keywords
image(11), fingerprint(10), processing(6), algorithm(6), pattern(4), minutiae(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-05-31
Volume 14, Issue 2, Year 2014, On page(s): 3 - 8
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.02001
Web of Science Accession Number: 000340868100001
SCOPUS ID: 84901844580

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In analysis of minutiae based fingerprint systems, fingerprints needs to be pre-processed. The pre-processing is carried out to enhance the quality of the fingerprint and to obtain more accurate minutiae points. Reducing the pre-processing time is important for identification and verification in real time systems and especially for databases holding large fingerprints information. Parallel processing and parallel CPU computing can be considered as distribution of processes over multi core processor. This is done by using parallel programming techniques. Reducing the execution time is the main objective in parallel processing. In this study, pre-processing of minutiae based fingerprint system is implemented by parallel processing on multi core computers using OpenMP and on graphics processor using CUDA to improve execution time. The execution times and speedup ratios are compared with the one that of single core processor. The results show that by using parallel processing, execution time is substantially improved. The improvement ratios obtained for different pre-processing algorithms allowed us to make suggestions on the more suitable approaches for parallelization.

References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 2]

[2] S. Gorgunoglu, A. Cavusoglu, "A Fast and Simple Algorithm for Fingerprint Segmentation", Engineering Science and Technology an International Journal (JESTECH) (formerly TEKNOLOJI), vol. 11, no. 2, pp. 87-92, 2008.

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[CrossRef] [Web of Science Times Cited 87]

[4] L. Hong, Y. Wan, A. Jain, "Fingerprint image enhancement: algorithm and performance evaluation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 777-789, 1998.
[CrossRef] [Web of Science Times Cited 1024]

[5] A. Cavusoglu, S. Gorgunoglu, "A fast fingerprint image enhancement algorithm using a parabolic mask", Computers & Electrical Engineering, vol. 34, no. 3, pp. 250-256, 2008.
[CrossRef] [Web of Science Times Cited 10]

[6] S. W. Smith, The Scientist and Engineer's Guide to Digital Signal Processing, San Diego: California Technical Publishing, pp. 436-442, 1999.

[7] V. Espinosa-Duro, "Fingerprints thinning algorithm", Aerospace and Electronic Systems Magazine IEEE, vol. 18, no. 9, pp. 28-30, 2003.
[CrossRef] [Web of Science Times Cited 12]

[8] M. Tico, P. Kuosmanen, "An algorithm for fingerprint image postprocessing", Thirty-Fourth Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1735 - 1739, 2000.

[9] Q. Xiao, H. Raafat, "A combined statistical and structural approach for fingerprint", IEEE International Conference on image postprocessing, Systems, Man and Cybernetics, 331-335, 1990.
[CrossRef] [Web of Science Record]

[10] J. C. Amengual, A. Juan, J. C. Perez, F. Prat, S. Saez, J. M. Vilar, "Real-time minutiae extraction in fingerprint images", Sixth International Conference on Image Processing and Its Applications, vol. 2, pp. 871 - 875, 1997.

[11] S. Kasei, M. Deriche, B. Boashash, "Fingerprint Minutiae Exraction using block-direction on reconstructed images", TENCON '97. IEEE Region 10 Annual Conference. Speech and Image Technologies for Computing and Telecommunications, vol. 1, pp. 303-306, 1997.

[12] Z. Shi, V. Govindaraju, "A chaincode based scheme for fingerprint minutiae exraction", Pattern Recognition Letters, vol. 27, no. 5, pp. 462-468, 2006.
[CrossRef] [Web of Science Times Cited 21]

[13] M. Gamassi, V. Piuri, F. Scotti, "Fingerprint local analysis for high-performance minutiae extraction", IEEE International Conference on Image Processing ICIP 2005, vol. 3, pp. III - 265-8, 2005.

[14] T. Xionggang, C. Jun, "Paralel Image Processing with OpenMP", The 2nd IEEE International Conference on Information Management and Engineering (ICIME), pp. 20-23, 2010.

[15] Y. Liu, F. Gao, "Parallel implementations of image processing algorithms on multi core", Fourth Internatinal Conference on Genetic and Evolutionary Computing, pp.71-74, 2010.

[16] E. Ramaraj, A. S. Rajan, "Median filter using open multiprocessing in agriculture", IEEE 10th International Conference on signal processing ICSP2010, pp. 42-45, 2010.

[17] H. Cao, X. Gu, "OpenMP Parallelization of Jacquin Fractal Image Encoding", 2010 International Conference on E-Product E-Service and E-Entertainment (ICEEE), pp. 1-4, 2010.

[18] S. Park, S. Ponce, J. Huang, Y. Cao, F. Quek, "Low-Cost, High Speed Computer Vision Using NVIDIA's CUDA Architecture", 37th IEEE Applied Imagery Pattern Recognition Workshop, 1-4 (2008)

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[21] Y. Li, K. Zhao, X. Chu, J. Liu, "Speeding up k-Means algorithm by GPUs", Journal of Computer and System Sciences, vol. 79, pp. 216-229, 2013.
[CrossRef] [Web of Science Times Cited 17]

[22] S. Akhter, J. Roberts, Multi-Core Programming, Intel Press, Hillsboro, pp. 13-14, 2006.

References Weight

Web of Science® Citations for all references: 1,173 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 51 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-06-17 09:54 in 121 seconds.

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Faculty of Electrical Engineering and Computer Science
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