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Reseach Article

Transformation based Approach of Combining Iris and Fingerprint Biometric at Confidence Level

by S. M. Rajbhoj, P. B. Mane
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 13
Year of Publication: 2015
Authors: S. M. Rajbhoj, P. B. Mane

S. M. Rajbhoj, P. B. Mane . Transformation based Approach of Combining Iris and Fingerprint Biometric at Confidence Level. International Journal of Computer Applications. 116, 13 ( April 2015), 1-5. DOI=10.5120/20393-2688

@article{ 10.5120/20393-2688,
author = { S. M. Rajbhoj, P. B. Mane },
title = { Transformation based Approach of Combining Iris and Fingerprint Biometric at Confidence Level },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 13 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { },
doi = { 10.5120/20393-2688 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:56:59.341981+05:30
%A S. M. Rajbhoj
%A P. B. Mane
%T Transformation based Approach of Combining Iris and Fingerprint Biometric at Confidence Level
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 13
%P 1-5
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

Unimodal Biometric systems depending on information from single trait has many limitations. These are noisy input, inability to enroll into system, unacceptable error rates, spoofing and universality of traits. Multibiometric systems is likely to enhance the recognition accuracy by integration the evidence presented by multiple sources of information. In this paper a multibiometric system using transformation based fusion of two most used biometric traits, fingerprint and iris at confidence level is proposed. Features are extracted from individual biometric modalities by efficient algorithm. These features are first matched with their corresponding templates to compute the corresponding match scores. Match scores obtained from different traits are then transformed using different techniques and combined by simple sum rule to generate a fused match score. The proposed framework is evaluated using standard database. This system overcomes limitation of unimodal biometric system and gives improved performance accuracy. An equal error rate achieved by this system is 0. 400. The benefit of this approach is that, it does not require any estimation as in density based approach or a large number of training score as in classifier based approach. Image or feature level fusion is expected to result in better performance, but this approach outperforms feature level as well as decision level fusion of iris and fingerprint.

  1. A. K. Jain, A. Ross, and S. Prabhakar, "An introduction to biometric recognition," IEEE Trans. on Circuits and Systems for Video Technology, vol. 14, pp. 4–20, Jan 2004.
  2. A. Ross and A. K. Jain, "Information fusion in biometrics,"Pattern Recognition Letters, vol. 24, pp. 2115–2125, Sep 2003.
  3. Brunelli, R. , Falavigna, D " Person Identification using multiple cues. " IEEE Trans. Pattern Analysis and Mach. Intell. 17, 955–966 (1995)
  4. Verlinde, P. , Cholet, G, " Comparing decision fusion paradigms using k-NN based classifiers, decision trees and logistic regression in a multi-modal identity verification application", In: Proceedings of Second International Conference on Audio- andVideo-Based Biometric Person Authentication (AVBPA), pp. 188–193. Washington D. C. , USA (1999)
  5. L. Hong and A. K. Jain, "Integrating faces and fingerprints for personal identification," IEEE Transactions on PAMI, vol. 20, pp. 1295–1307, Dec 1998.
  6. A. Kumar, D. C. M. Wong, H. C. Shen, and A. K. Jain, "Personal verification using palmprint and hand geometry biometric," in Proc. of 4th Int'l Conf. on Audio and Video-based Biometric Person Authentication (AVBPA), (Guildford, UK), pp. 668–678, Jun 2003.
  7. Sanderson, C. , Paliwal, K. K " Information Fusion and Person Verification Using Speech and Face Information". Research Paper IDIAP-RR 02-33, IDIAP (2002)
  8. Y. Wang, T. Tan, and A. K. Jain. "Combining Face and Iris Biometrics for Identity Verification. ", In Fourth International Conference on Audio- and Video-based Biometric Person Authentication (AVBPA), Guildford, UK, pages 805-813, June 2003
  9. Asim Baig, Ahmed Bouridane, Fattih K. , Gang Qu, "Fingerprint-Iris Fusion based Identification System using a Single Hamming Distance Matcher. ",International Journal of Bio-Science and Bio-Technology, Vol 1, No. 1, Dec 2009.
  10. V. Conte,C. Militello, F Sorbello, "A Frequency based approach for Feature Fusion in Fingerprint and Iris Multimodal Biometric Identification Systems", IEEE Transactions of System, Man and Cybernetics, vol-40, No. 4, July2010.
  11. Lumini and Nanni, "When Fingerprint are combined with Iris – A Case Study: FVC2004 and CASIA " International Journal of Network Security, Vol. 4 No. 1, pp27-34,Jan 2007
  12. L. Hong, Y. Wan, & A. K. Jain, Fingerprint Image Enhancement: Algorithm and Performance Evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 1998, 777-789
  13. S. M Rajbhoj and P. B. Mane,"An Improved binarization based algorithm using minutiae approach for Fingerprint Identification" International Journal of Engineering and Advanced Technology (IJEAT) Vol-1, Issue-6, August 2012
  14. J. G. Daugman, "How Iris Recognition Works", In IEEE Transactions on circuits and systems for video technology, vol. 14, no. 1,January 2004
  15. Libor Masek, "Recognition of Human Iris Patterns for Biometric Identification", Thesis Report School of Computer Science and Software Engineering, Western Australia, 2003
  16. Rossani F. , Eslava M. T. , Ea T. , Aml F. , Amara A. , "Iris Identification and robustness evaluation of wavelet packetsbased algorithm", IEEE International Conference on image processing, vol. 3, pp. III -257-260
  17. S. M. Rajbhoj and P. B. Mane, "Haar Wavelet Approach of Iris Texture Extraction for personal Recognition", International Journal of Innovative Technology and Exploring Engineering, Vol. 3 Issue 2, July – 2013
  18. A. K. Jain, K. Nandakumar, and A. Ross. Score Normalization in Multimodal Biometric Systems. Pattern Recognition, 38(12):2270{2285, December 2005.
  19. Fingerprint verification competition. http://bias. csr. unibo. it/fvc2004
  20. CASIA Iris Image Database http://www. cbsr. ia. ac. cn/irisdatabase
Index Terms

Computer Science
Information Sciences


Biometric unimodal multibiometric match score confidence level fusion ROC.