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| Reducing Process-Time for Fingerprint Identification System
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Source |
International Journal of Biometrics and Bioinformatics (IJBB) |
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Table of Contents |
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Complete Issue PDF(917.73KB) |
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Volume: 3 Issue: 1 |
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Pages: 1-18 |
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Publication
Date: February 2009 |
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ISSN
(Online): 1985-2347 |
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1 - 9 |
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Author(s) |
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Published
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15-03-2009 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: Biometrics identification, verification , minutiae points , singular points |
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| Fingerprints are the most widely used biometric feature for person identification and verification in the field of biometric identification. Fingerprints possess two main types of features that are used for automatic fingerprint identification and verification: (i) Ridge and furrow structure that forms a special pattern in the central region of the fingerprint and (ii) Minutiae details associated with the local ridge and furrow structure. In a traditional biometric recognition system, the biometric template is usually stored on a central server during enrollment. The candidate biometric template captured by the biometric device is sent to the server where the processing and matching steps are performed. This paper presents an approach to speed up the matching process by classifying the fingerprint pattern into different groups at the time of enrollment, and improves fingerprint matching while matching the input template with stored template. To solve the problem, we take several aspects into consideration like classification of fingerprint, singular points. The algorithm result indicates that this approach manages to speed up the matching effectively, and therefore prove to be suitable for large database like forensic divisions. |
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| 1 |
D. Maltoni, D. Maio, A. K. Jain, S. Prabhakar. “Handbook of Fingerprint Recognition”. Springer- Verlag, 2003.
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H. C. Lee and R. E. Gaensslen, Advances in Fingerprint Technology, Elsevier, New York, 1991.
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American university of beirut faculty of engineering and architecture department of electrical and computer engineering eece695c – adaptive filtering and neural networks fingerprint identification–project2
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Biometrics Information Group www.biometricsinfo.org.
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A. K. Jain, R. Bolle, S. Pankanti (eds), Biometrics: Personal Identification in Networked Society, Kluwer Academic, December 1998.
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A. K. Hrechak and J. A. McHugh, Automated Fingerprint Recognition using Structural Matching, Pattern Recognition, Vol. 23, No. 8, 1990.
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Manuals, Forensic Science Laboratory, Madhuban, Karnal
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The Henry Classification System Copyright © 2003 International Biometric Group
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9. L. Hong, Automatic Personal Identification Using Fingerprints, PhD Thesis, Michigan State University,1998
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Anil Jain Sharath Pankanti, Fingerprint Classification and Matching 2004.
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R. Bansal, P. Sehga and P. Bedi, “Effective Morphological Extraction of True Fingerprint Minutiae based on the Hit or Miss Transform” International Journal of Biometrics and Bioinformatics (IJBB), 4(2), pp. 71 – 85, May. 2010.
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V. M. Mane and D.V.Jadhav, “Review of Multimodal Biometrics: Applications, Challenges and Research Areas” International Journal of Biometrics and Bioinformatics (IJBB), 3(5), pp. 66-95, Nov. 2009.
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A. K. Sharma, P. Sharma and A. Ganpati, “Comparing the Efficiency of Minutia Based and Improved Fingercode Fingerprint Algorithm” International Journal of Computer Science and Information Technologies (IJCSIT), 2(4), pp. 1398-1399, 2011.
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| 1 |
asaha
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Dr. Chander Kant
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REDDY’s FORENSIC PAGE
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| Chander Kant : Colleagues
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| Rajender Nath : Colleagues
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