About this Journal Submit a Manuscript Table of Contents
Applied Computational Intelligence and Soft Computing
Volume 2013 (2013), Article ID 515918, 11 pages
http://dx.doi.org/10.1155/2013/515918
Research Article

A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification

1Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India
2Department of Computer Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India
3Nagar Yuwak Shikshan Sanstha, Nagpur, India

Received 1 April 2013; Revised 16 June 2013; Accepted 17 June 2013

Academic Editor: Zhang Yi

Copyright © 2013 Ujwalla Gawande et al. 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.

Linked References

  1. S. Soviany and M. Jurian, “Multimodal biometric securing methods for informatic systems,” in Proceedings of the 34th International Spring Seminar on Electronic Technology, pp. 12–14, Phoenix, Ariz, USA, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Soviany, C. Soviany, and M. Jurian, “A multimodal approach for biometric authentication with multiple classifiers,” in International Conference on Communication, Information and Network Security, pp. 28–30, 2011.
  3. F. Besbes, H. Trichili, and B. Solaiman, “Multimodal biometric system based on fingerprint identification and iris recognition,” in Proceedings of the 3rd International Conference on Information and Communication Technologies: From Theory to Applications (ICTTA '08), pp. 1–5, Damascus, Syria, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Baig, A. Bouridane, F. Kurugollu, and G. 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, pp. 47–58, 2009. View at Scopus
  5. L. Zhao, Y. Song, Y. Zhu, C. Zhang, and Y. Zheng, “Face recognition based on multi-class SVM,” in IEEE International Conference on Control and Decision, pp. 5871–5873, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. T. C. Mota and A. C. G. Thomé, “One-against-all-based multiclass svm strategies applied to vehicle plate character recognition,” in IEEE International Joint Conference on Neural Networks (IJCNN '09), pp. 2153–2159, New York, NY, USA, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. R. Brunelli and D. Falavigna, “Person identification using multiple cues,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 10, pp. 955–966, 1995. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Theodoridis and K. Koutroumbas, Pattern Recognition, Elsevier, 4th edition, 2009.
  9. B. Gokberk, A. A. Salah, and L. Akarun, “Rank-based decision fusion for 3D shape-based face recognition,” in Proceedings of the 13th IEEE Conference on Signal Processing and Communications Applications, pp. 364–367, Antalya, Turkey, 2005.
  10. A. Jain and A. Ross, “Fingerprint mosaicking,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '02), pp. IV/4064–IV/4067, Rochester, NY, USA, May 2002. View at Scopus
  11. M. Sepasian, W. Balachandran, and C. Mares, “Image enhancement for fingerprint minutiae-based algorithms using CLAHE, standard deviation analysis and sliding neighborhood,” in IEEE Transactions on World Congress on Engineering and Computer Science, pp. 1–6, 2008.
  12. B. S. Xiaomei Liu, Optimizations in iris recognition [Ph.D. thesis], Computer Science and Engineering Notre Dame, Indianapolis, Ind, USA, 2006.
  13. J. Daugman, “How iris recognition works,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21–30, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. L. Hong and A. Jain, “Integrating faces and fingerprints for personal identification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 12, pp. 1295–1307, 1998. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Fierrez-aguilar, L. Nanni, J. Ortega-garcia, and D. Maltoni, “Combining multiple matchers for fingerprint verification: a case study,” in Proceedings of the International Conference on Image Analysis and Processing (ICIAP '05), vol. 3617 of Lecture Notes in Computer Science, pp. 1035–1042, Springer, 2005.
  16. A. Lumini and L. Nanni, “When fingerprints are combined with Iris—a Case Study: FVC2004 and CASIA,” International Journal of Network Security, vol. 4, no. 1, pp. 27–34, 2007.
  17. L. Hong, A. K. Jain, and S. Pankanti, “Can multibiometrics improve performance?” in IEEE Workshop on Automatic Identification Advanced Technologies, pp. 59–64, New Jersey, NJ, USA, 1999.
  18. A. Jagadeesan, T. Thillaikkarasi, and K. Duraiswamy, “Protected bio-cryptography key invention from multimodal modalities: feature level fusion of fingerprint and Iris,” European Journal of Scientific Research, vol. 49, no. 4, pp. 484–502, 2011. View at Scopus
  19. I. Raglu and P. P. Deepthi, “Multimodal Biometric Encryption Using Minutiae and Iris feature map,” in Proceedings of IEEE Students’International Conference on Electrical, Electronics and Computer Science, pp. 94–934, Zurich, Switzerland, 2012.
  20. V. C. Subbarayudu and M. V. N. K. Prasad, “Multimodal biometric system,” in Proceedings of the 1st International Conference on Emerging Trends in Engineering and Technology (ICETET '08), pp. 635–640, Nagpur, India, July 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Nagesh kumar, P. K. Mahesh, and M. N. Shanmukha Swamy, “An efficient secure multimodal biometric fusion using palmprint and face image,” International Journal of Computer Science, vol. 2, pp. 49–53, 2009.
  22. F. Yang and B. Ma, “A new mixed-mode biometrics information fusion based-on fingerprint, hand-geometry and palm-print,” in Proceedings of the 4th International Conference on Image and Graphics (ICIG '07), pp. 689–693, Jinhua, China, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. A. J. Basha, V. Palanisamy, and T. Purusothaman, “Fast multimodal biometric approach using dynamic fingerprint authentication and enhanced iris features,” in Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC '10), pp. 1–8, Coimbatore, India, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. M. M. Monwar and M. L. Gavrilova, “Multimodal biometric system using rank-level fusion approach,” IEEE Transactions on Systems, Man and Cybernetics B, vol. 39, no. 4, pp. 867–878, 2009. View at Scopus
  25. V. Conti, C. Militello, F. Sorbello, and S. Vitabile, “A frequency-based approach for features fusion in fingerprint and iris multimodal biometric identification systems,” IEEE Transactions on Systems, Man and Cybernetics C, vol. 40, no. 4, pp. 384–395, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. G. Aguilar, G. Sánchez, K. Toscano, M. Nakano, and H. Pérez, “Multimodal biometric system using fingerprint,” in International Conference on Intelligent and Advanced Systems (ICIAS '07), pp. 145–150, Kuala Lumpur, Malaysia, November 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. L. Lin, X.-F. Gu, J.-P. Li, L. Jie, J.-X. Shi, and Y.-Y. Huang, “Research on data fusion of multiple biometric features,” in International Conference on Apperceiving Computing and Intelligence Analysis (ICACIA '09), pp. 112–115, Chengdu, China, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. D. E. Maurer and J. P. Baker, “Fusing multimodal biometrics with quality estimates via a Bayesian belief network,” Pattern Recognition, vol. 41, no. 3, pp. 821–832, 2008. View at Publisher · View at Google Scholar · View at Scopus
  29. T. Ko, “Multimodal biometric identification for large user population using fingerprint, face and IRIS recognition,” in Proceedings of the 34th Applied Imagery and Pattern Recognition Workshop (AIPR '05), pp. 88–95, Arlington, Va, USA, 2005.
  30. K. Nandakumar and A. K. Jain, “Multibiometric template security using fuzzy vault,” in IEEE 2nd International Conference on Biometrics: Theory, Applications and Systems, pp. 198–205, Washington, DC, USA, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. A. Nagar, K. Nandakumar, and A. K. Jain, “Multibiometric cryptosystems based on feature-level fusion,” IEEE Transactions on Information Forensics and Security, vol. 7, no. 1, pp. 255–268, 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. K. Balasubramanian and P. Babu, “Extracting minutiae from fingerprint images using image inversion and bi-histogram equalization,” in SPIT-IEEE Colloquium and International Conference on Biometrics, pp. 53–56, Washington, DC, USA, 2009.
  33. Y.-P. Huang, S.-W. Luo, and E.-Y. Chen, “An efficient iris recognition system,” in Proceedings of the International Conference on Machine Learning and Cybernetics, pp. 450–454, Beijing, China, November 2002. View at Scopus
  34. N. Tajbakhsh, K. Misaghian, and N. M. Bandari, “A region-based Iris feature extraction method based on 2D-wavelet transform,” in Bio-ID-MultiComm, 2009 joint COST 2101 and 2102 International Conference on Biometric ID Management and Multimodal Communication, vol. 5707 of Lecture Notes in Computer Science, pp. 301–307, 2009.
  35. D. Zhang, F. Song, Y. Xu, and Z. Liang, Advanced Pattern Recognition Technologies with Applications to Biometrics, Medical Information Science Reference, New York, NY, USA, 2008.
  36. 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. View at Scopus
  37. M. M. Ramon, X. Nan, and C. G. Christodoulou, “Beam forming using support vector machines,” IEEE Antennas and Wireless Propagation Letters, vol. 4, pp. 439–442, 2005.
  38. M. J. Fernández-Getino García, J. L. Rojo-Álvarez, F. Alonso-Atienza, and M. Martínez-Ramón, “Support vector machines for robust channel estimation in OFDM,” IEEE Signal Processing Letters, vol. 13, no. 7, pp. 397–400, 2006. View at Publisher · View at Google Scholar · View at Scopus
  39. B. E. Boser, I. M. Guyon, and V. N. Vapnik, “Training algorithm for optimal margin classifiers,” in Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory (COLT '92), pp. 144–152, Morgan Kaufmann, San Mateo, Calif, USA, July 1992. View at Scopus
  40. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1999.
  41. C.-W. Hsu and C.-J. Lin, “A simple decomposition method for support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 291–314, 2002. View at Publisher · View at Google Scholar · View at Scopus
  42. J. Bhatnagar, A. Kumar, and N. Saggar, “A novel approach to improve biometric recognition using rank level fusion,” in IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–6, Hong Kong, China, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  43. J. Manikandan and B. Venkataramani, “Evaluation of multiclass support vector machine classifiers using optimum threshold-based pruning technique,” IET Signal Processing, vol. 5, no. 5, pp. 506–513, 2011. View at Publisher · View at Google Scholar · View at Scopus
  44. R. Noori, M. A. Abdoli, A. Ameri Ghasrodashti, and M. Jalili Ghazizade, “Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: a case study of mashhad,” Environmental Progress and Sustainable Energy, vol. 28, no. 2, pp. 249–258, 2009. View at Publisher · View at Google Scholar · View at Scopus