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BY-NC-ND 3.0 license Open Access Published by De Gruyter January 11, 2009

Multimodal biometric authentication based on score level fusion using support vector machine

  • F. Wang EMAIL logo and J. Han
From the journal Opto-Electronics Review

Abstract

Fusion of multiple biometrics for human authentication performance improvement has received considerable attention. This paper presents a novel multimodal biometric authentication method integrating face and iris based on score level fusion. For score level fusion, support vector machine (SVM) based fusion rule is applied to combine two matching scores, respectively from Laplacianface based face verifier and phase information based iris verifier, to generate a single scalar score which is used to make the final decision. Experimental results show that the performance of the proposed method can bring obvious improvement comparing to the unimodal biometric identification methods and the previous fused face-iris methods.

[1] A.K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition”, IEEE T. Circ. Syst. Vid. 14, 4–20 (2004). http://dx.doi.org/10.1109/TCSVT.2003.81834910.1109/TCSVT.2003.818349Search in Google Scholar

[2] A.K. Jain and A. Ross, “Multibiometric systems”, Commun. ACM 47, 34–40 (2004). http://dx.doi.org/10.1145/962081.96210210.1145/962081.962102Search in Google Scholar

[3] Z. Liu and S. Sarkar, “Outdoor recognition at a distance by fusing gait and face”, Image Vision Comput. 25, 817–832 (2007). http://dx.doi.org/10.1016/j.imavis.2006.05.02210.1016/j.imavis.2006.05.022Search in Google Scholar

[4] A. Ross and A.K. Jain, “Information fusion in biometrics”, Pattern Recogn. Lett. 24, 2115–2125 (2003). http://dx.doi.org/10.1016/S0167-8655(03)00079-510.1016/S0167-8655(03)00079-5Search in Google Scholar

[5] Y. Wang, T. Tan, Y. Wang, and D. Zhang, “Combining face and iris biometric for identity verification”, Proc. 4th Int. Conf. on Audio- and Video-Based Biometric Person Authentication (AVBPA) 1, 805–813 (2003). http://dx.doi.org/10.1007/3-540-44887-X_9310.1007/3-540-44887-X_93Search in Google Scholar

[6] C. Chen and C. Chu, “Fusion of face and iris features for multimodal biometrics”, Lect. Notes Comput. Sc. 3832, 571–580 (2006). http://dx.doi.org/10.1007/11608288_7610.1007/11608288_76Search in Google Scholar

[7] H. Xiaofei, Y. Shuicheng, H. Yuxiao, P. Niyogi, and Z. Hong-Jiang, “Face recognition using Laplacianfaces”, IEEE T. Pattern Anal. 27, 328–340 (2005). http://dx.doi.org/10.1109/TPAMI.2005.5510.1109/TPAMI.2005.55Search in Google Scholar PubMed

[8] Y. Du, “Using 2D log-gabor spatial filters for iris recognition”, Biometric Technology for Human Identification 6202, 1–8 (2006). 10.1117/12.663834Search in Google Scholar

[9] F. Wang and J. Han, “Iris recognition method using Log—Gabor filtering and feature fusion”, J. Xi’an Jiaotong Univ. 41, 889–893 (2007). Search in Google Scholar

[10] M. Yang, D.J. Kriegman, and N. Ahuja, “Detecting faces in images: A survey”, IEEE T. Pattern Anal. 24, 34–58 (2002) http://dx.doi.org/10.1109/34.98288310.1109/34.982883Search in Google Scholar

[11] P.N. Belhumeur, J.P. Hepanha, and D.J. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection”, IEEE T. Pattern Anal. 19, 711–720 (1997). http://dx.doi.org/10.1109/34.59822810.1109/34.598228Search in Google Scholar

[12] D. Cai, X. He, J. Han, and H.J. Zhang, “Orthogonal Laplacianfaces for face recognition”, IEEE T. Image Process. 15, 3608–3614 (2006). http://dx.doi.org/10.1109/TIP.2006.88194510.1109/TIP.2006.881945Search in Google Scholar

[13] J. Daugman, “How iris recognition works”, IEEE T. Circ. Syst. Vid. 14, 21–30 (2004). http://dx.doi.org/10.1109/TCSVT.2003.81835010.1109/TCSVT.2003.818350Search in Google Scholar

[14] J. Daugman, “The importance of being random: Statistical principles of iris recognition”, Lect. Notes Comput. Sc. 36, 279–291 (2003). Search in Google Scholar

[15] B. Scholkopf and A.J. Smola, Learning with Kernels, MIT Press, Cambridge, MA, 2002. Search in Google Scholar

[16] S. Keerthi, S.K. Shevade, and C. Bhattacharyya, “Improvements to Platt’s SMO algorithm for SVM classifier design”, Neural Computation 13, 637–649 (2001). http://dx.doi.org/10.1162/08997660130001449310.1162/089976601300014493Search in Google Scholar

[17] AT&T Laboratories Cambridge, The ORL Database of Faces: http://www.cam-orl.co.uk/facedatabase.html Search in Google Scholar

[18] H. Proenca and A. Alexandre, UBIRIS Iris Image Database: http://iris.di.ubi.pt Search in Google Scholar

Published Online: 2009-1-11
Published in Print: 2009-3-1

© 2009 SEP, Warsaw

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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