Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(564.74KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Reducing Process-Time for Fingerprint Identification System
Chander Kant, Rajender Nath
Pages - 1 - 9     |    Revised - 20-02-2009     |    Published - 15-03-2009
Volume - 3   Issue - 1    |    Publication Date - February 2009  Table of Contents
MORE INFORMATION
KEYWORDS
Biometrics identification, verification , minutiae points , singular points
ABSTRACT
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.
CITED BY (29)  
1 Selvakumar, M., & Nedumaran, D. (2015). Implementation of modified polar complex moments–based fingerprint orientation estimation for effective segmentation. International Journal of Biometrics, 7(1), 45-68.
2 Zahedi, M., & Ghadi, O. R. (2015). Combining Gabor filter and FFT for fingerprint enhancement based on a regional adaption method and automatic segmentation. Signal, Image and Video Processing, 9(2), 267-275.
3 Celik, N., Manivannan, N., Balachandran, W., & Kosunalp, S. (2015). Multimodal Biometrics for Robust Fusion Systems using Logic Gates.
4 Prabu, U., Priyadharshini, G., Saranya, M., Parveen, N. R., Shanmugam, M., & Amudhavel, J. (2015, March). Efficient personal identification using multimodal biometrics. In Circuit, Power and Computing Technologies (ICCPCT), 2015 International Conference on (pp. 1-5). IEEE.
5 Sankareswari, K. (2014). A Comparative Study on Biometric System Authentication Security and Usability. International Journal, 1(2).
6 John Chriisttopher, A. (2014). Multiibiometric human recognition system fusion of face palmprint and touchless fingerpriint traits.
7 Ami-Narh, J., Aziale, L. K., & Akanferi, A. (2014). The Adoption of Biometric Fingerprint Timekeeping Technology in the Ghanaian Business Community-Effectiveness and Impact. International Journal of Computer Applications, 85(9).
8 Kasban, H., & Authority, A. E. (2014). An Accurate and Fast Automatic Fingerprints Verification Technique.
9 Amin, A. E., & Elgamal, A. F. (2014). Performance Improvement for Fingerprint Recognition System using Shape and Orientation Descriptors. International Journal of Advanced Research in Computer Science, 5(8).
10 AKROUF, S. (2014). Une approche multimodale pour l’identification du locuteur (Doctoral dissertation).
11 Kumar, S., Kumar, V., & Singh, A. An Analytical Study on integration of Multibiometric Traits at Matching Score Level using Transformation Techniques.
12 Bhadane, M. V. R., Deshpande, M. V., & Mishra, M. P. 2D Fingerprint Matching using Multi-dimensional Artificial Neural Networks.
13 Parlakyildiz, S., & Hardalaç, F. (2013). A New and Effective Method in Fingerprint Classification. Life Science Journal, 10(12s).
14 Jawale, J. B., & Jawale, M. P. J. (2013). Image Fusion Techniques in Multimodal Biometrics Systems, Applications and Challenges. Image, 17(17).
15 Parvathi, R. (2013). Efficient Fingerprint Recognition System Using Pseudo 2D Hidden Markov Model.
16 Patil, S. B. (2012). A Study of Biometric, Multimodal Biometric Systems: Fusion Techniques, Applications and Challenges. IJCST, 3(1), 524-526.
17 Zhang, Y., Rasku, J., & Juhola, M. (2012). Biometric verification of subjects using saccade eye movements. International Journal of Biometrics, 4(4), 317-337.
18 Gudavalli, M., Raju, S. V., & Kumar, D. S. (2012, March). Multimodal Biometrics--Sources, Architecture and Fusion Techniques: An Overview. In Biometrics and Security Technologies (ISBAST), 2012 International Symposium on (pp. 27-34). IEEE.
19 MM, K., YS, R., SB, D., NNH, A. D., & KV, K. (2012). multimodal biometric system using face and signature: a score level fusion approach.
20 Olaniyi, O. M., Omotosho, A., Oluwatosin, E. A., Towolawi, O. K., & Grant-Ezeronye, G. C. (2012). Application of Information Communication Technology to the Management of Library’s Readers’ Desk. DESIDOC Journal of Library & Information Technology, 32(6).
21 Barde, S., Khobragade, S., & Singh, R. (2012). Authentication Progression through Multimodal Biometric System. Biometrics, 2(3).
22 Sasikala, T. S., & Celin, J. J. A. Secured Access of Locker using Multimodal Biometrics.
23 Parvathi, R., & Sankar, M. Fingerprint Authentication System using Hybrid Classifiers. International Journal of Soft Computing and Engineering (IJSCE) ISSN, 2231-2307.
24 SINGLA, S. K. (2011). Biometric Security Solutions for Human Authentication (Doctoral dissertation, thapar university, patiala).
25 Gudavalli, M., Raju, S. V., & Kumar, D. S. (2011). An Overview on MultiModal Biometrics-Sources, Architecture & Fusion Techniques. Biometrics and Bioinformatics, 3(11), 516-519.
26 Agarwal, S. R., Kokadwar, D. R., Kauser, Z., & Apte, G. (2011). Multimodal Biometrics System-Applications, Challenges and Research Areas. BIOINFO Human-Computer Interaction, 1(1).
27 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.
28 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.
29 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.
1 Google Scholar
2 Academic Journals Database
3 ScientificCommons
4 Academic Index
5 CiteSeerX
6 refSeek
7 iSEEK
8 Socol@r
9 ResearchGATE
10 Libsearch
11 Bielefeld Academic Search Engine (BASE)
12 Scribd
13 WorldCat
14 SlideShare
15 PDFCAST
16 PdfSR
17 2dix.com
18 printfu
1 D. Maltoni, D. Maio, A. K. Jain, S. Prabhakar. “Handbook of Fingerprint Recognition”. Springer- Verlag, 2003.
2 H. C. Lee and R. E. Gaensslen, Advances in Fingerprint Technology, Elsevier, New York, 1991.
3 American university of beirut faculty of engineering and architecture department of electrical and computer engineering eece695c – adaptive filtering and neural networks fingerprint identification–project2
4 Biometrics Information Group www.biometricsinfo.org.
5 A. K. Jain, R. Bolle, S. Pankanti (eds), Biometrics: Personal Identification in Networked Society, Kluwer Academic, December 1998.
6 A. K. Hrechak and J. A. McHugh, Automated Fingerprint Recognition using Structural Matching, Pattern Recognition, Vol. 23, No. 8, 1990.
7 Manuals, Forensic Science Laboratory, Madhuban, Karnal
8 The Henry Classification System Copyright © 2003 International Biometric Group
9 9. L. Hong, Automatic Personal Identification Using Fingerprints, PhD Thesis, Michigan State University,1998
10 Anil Jain Sharath Pankanti, Fingerprint Classification and Matching 2004.
Mr. Chander Kant
K.U. Kurukshetra - India
ckverma@rediffmail.com
Dr. Rajender Nath
K.U. Kurukshetra - India