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Feature Level Fusion in Multimodal Biometric Authentication System

by M. Fathima Nadheen, S. Poornima
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 18
Year of Publication: 2013
Authors: M. Fathima Nadheen, S. Poornima
10.5120/12074-8264

M. Fathima Nadheen, S. Poornima . Feature Level Fusion in Multimodal Biometric Authentication System. International Journal of Computer Applications. 69, 18 ( May 2013), 36-40. DOI=10.5120/12074-8264

@article{ 10.5120/12074-8264,
author = { M. Fathima Nadheen, S. Poornima },
title = { Feature Level Fusion in Multimodal Biometric Authentication System },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 18 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number18/12074-8264/ },
doi = { 10.5120/12074-8264 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:30:38.616482+05:30
%A M. Fathima Nadheen
%A S. Poornima
%T Feature Level Fusion in Multimodal Biometric Authentication System
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 18
%P 36-40
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Multimodal systems integrate multiple sources of human information to ensure high level security. Multimodal biometric systems increase the recognition rate of the biometric systems either by reducing the false acceptance rate (FAR) or false rejection rate (FRR). Multiple biometric traits can be combined at feature level. Feature level fusion increases the reliability of the system by preventing the biometric template from modification. In the proposed system, feature level fusion is employed to fuse the feature vectors of iris and ear extracted by Principal Component Analysis technique, which also reduces the dimension of the feature vectors. Finally matching is performed by comparing the test fused feature vectors with all training images using distance measure. This system is developed to study and analyze, whether the performance of multimodal biometric system is improved over unimodal biometric system by attaining 93% success rate when fusion is inclined.

References
  1. Ashish Mishra, "Multimodal Biometrics it is: Need for Future Systems," International Journal of Computer Applications, vol. 3, pp. 28-33, June 2010.
  2. Mingxing He , Shi-JinnHorng , Pingzhi Fan , Ray-Shine Run , Rong-Jian Chen , Jui-Lin Lai , Muhammad Khurram Khan, Kevin Octavius Sentosa , "Performance evaluation of score level fusion in multimodal biometric systems," Pattern Recognition, vol. 43, pp. 1789-1800, May 2010.
  3. Shi-Jinn Horng, "An Improved Score Level Fusion in Multimodal Biometric Systems," International Conference on Parallel and Distributed Computing, Applications and Technologies, pp. 239 - 246, Dec 8-11, 2009.
  4. Nazmeen Bibi Boodoo, R K Subramanian, "Robust Multi-biometric Recognition Using Face and Ear Images," International Journal of Computer Science and Information Security, Vol. 6, No. 2, 2009.
  5. R. Mishra, V. Pathak, "Human Recognition using fusion of Iris and Ear Data," International Conference on Methods and Models in Computer Science, pp. 1-5, December 2009.
  6. A. Ross and R. Govindarajan, "Feature Level Fusion in Biometric Systems," SPICE Conference on Biometric Technology for human identification. . II, Vol. 5779, Orlando USA, March 2005, pp. 196-204.
  7. A. Ross and R. Govindarajan, "Feature Level Fusion Using Hand and Face Biometrics", Proceedings of SPIE Conference on Biometric Technology for Human Identification II, Orlando, USA, pp. 196-204, March 2005.
  8. A. Rattani, D. R. Kisku, M. Bicego, and M. Tistarelli, "Feature Level Fusion of Face and Fingerprint Biometrics," 1st IEEE International Conference on Biometrics, Theory, Applications and Systems, pp. 1 – 6, September 2007
  9. Suryanti Awang, Rubiyah Yusof, "Fusion of Face and Signature at the Feature Level by using Correlation Pattern Recognition", World Academy of Science, Engineering and Technology, 2011.
  10. A. Iannarelli. "Ear Identification Forensic Identification Series", Paramount Publishing Company, Fremont, California. 1989.
  11. Micha? Choras, "Ear Biometrics Based on Geometrical Feature Extraction," Electronic Letters on Computer Vision and Image Analysis 5, pp. 84-95, 2005.
  12. Li Yuan, Zhi chun Mu, "Ear recognition based on local information fusion," Pattern Recognition Letters 33(2), pp. 182-190, 2012.
  13. Shashi Kumar D R, K B Raja, R. K Chhootaray, Sabyasachi Pattnaik, "PCA based Iris Recognition using DWT," Int. J. Comp. Tech. Appl. , vol 2 (4), pp. 884-893, July-August 2011.
  14. Naveen Singh, Dilip Gandhi, Krishna Pal Singh, "Iris recognition system using a canny edge detection and a circular Hough transform," International Journal of Advances in Engineering & Technology, May 2011.
  15. A. Basit, M. Y. Javed, M. A. Anjum, "Efficient Iris Recognition Method for Human Identification," proceedings of world academy of science, engineering and technology, vol. 4 feb. 2005.
  16. John Daugman, "How Iris Recognition Works", in Proceedings of International Conference on Image Processing, vol. 1, pp. I-33- I-36, 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Feature level fusion Genuine Acceptance Rate Morphological operation Principal Component Analysis

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