- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Reviewers Acknowledgment
- Submit a Manuscript
- Subscription Information
- Table of Contents
Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 242401, 7 pages
Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study
Informatics and Telematics Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece
Received 29 October 2011; Accepted 12 January 2012
Academic Editor: Cheng-Jian Lin
Copyright © 2012 I. G. Damousis and S. Argyropoulos. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- A. Ross and A. Jain, “Information fusion in biometrics,” Pattern Recognition Letters, vol. 24, no. 13, pp. 2115–2125, 2003.
- A. K. Jain and A. Ross, “Multibiometric systems,” Communications of the ACM, vol. 47, no. 1, pp. 34–40, 2004.
- I. G. Damousis, D. Tzovaras, and E. Bekiaris, “Unobtrusive multimodal biometric authentication: the HUMABIO project concept,” Eurasip Journal on Advances in Signal Processing, vol. 2008, Article ID 265767, 11 pages, 2008.
- C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121–167, 1998.
- N. Christianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, 2000.
- C. W. Hsu, C. C. Chang, and C. J. Lin, “A practical guide to support vector classification,” Test, vol. 1, no. 1, pp. 1–16, 2010.
- R. E. Fan, P. H. Chen, and C. J. Lin, “Working set selection using second order information for training support vector machines,” Journal of Machine Learning Research, vol. 6, pp. 1889–1918, 2005.
- T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man and Cybernetics, vol. 15, no. 1, pp. 116–132, 1985.
- I. G. Damousis and D. Tzovaras, “Fuzzy fusion of eyelid activity indicators for hypovigilance-related accident prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 3, pp. 491–500, 2008.
- D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, New York, NY, USA, 1989.
- M. A. T. Figueiredo and A. K. Jain, “Unsupervised learning of finite mixture models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 381–396, 2002.
- T. Masters, Signal and Image Processing with Neural Networks, John Wiley & Sons, 1994.
- N. Poh and S. Bengio, “A score-level fusion benchmark database for biometric authentication,” in Proceedings of the 5th International Conference on Audio, and Video-Based Biometric Person Authentication (AVBPA '05), vol. 3546 of Lecture Notes in Computer Science, pp. 1059–1070, July 2005.
- XM2VTS database, http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/.
- L. Shoushan and Z. Chengqing, “Classifier combining rules under independence assumptions,” in Proceedings of the 7th international Conference on Multiple Classifier Systems (MCS '07), vol. 4472 of lecture Notes in Computer Science, pp. 322–332, 2007.
- HUMABIO project, http://www.humabio-eu.org/.
- A. Vatakis, et al., “Deliverable 7.1 HUMABIO Pilot plans,” 2008.