Person Authentication using Relevance Vector Machine (RVM) for Face and Fingerprint

Person Authentication using Relevance Vector Machine (RVM) for Face and Fingerprint

Автор: Long B. Tran, Thai H. Le

Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs

Статья в выпуске: 5 vol.7, 2015 года.

Бесплатный доступ

Multimodal biometric systems have proven more efficient in personal verification or identification than single biometric ones, so it is also a focus of this paper. Particularly, in the paper, the authors present a multimodal biometric system in which features from face and fingerprint images are extracted using Zernike Moment (ZM), the personal authentication is done using Relevance Vector Machine (RVM) and feature-level fusion technique. The proposed system has proven its remarkable ability to overcome the limitations of uni-modal biometric systems and to tolerate local variations in the face or fingerprint image of an individual. Also, the achieved experimental results have demonstrated that using RVM can assure a higher level of forge resistance and enables faster authentication than the state-of-the-art technique , namely the support vector machine (SVM).

Еще

Multimodal Biometric, Feature Level Fusion, Face, Fingerprint, Recognition System, Relevance vector machine, Zernike moment

Короткий адрес: https://sciup.org/15014754

IDR: 15014754

Список литературы Person Authentication using Relevance Vector Machine (RVM) for Face and Fingerprint

  • J. Campbell Jr., L. Alyea, and J. Dunn. Biometric security: Government application and operations. http://www.vitro.bloomington.in.us:8080/~BC/,1996.
  • S.G.Davies. Touching big brother: How biometric technology will fuse flesh and machine. Information Technology @ People, 7(4):60-69, 1994.
  • E. Newham. The Biometric Report. SJB Services, New York, 1995.
  • K. Jain, L. Hong and S. Pankanti, Biometrics: Promising Frontiers for Emerging Identification Market, Comm. ACM , pp. 91-98, Feb. 2000.
  • R.Clarke. Human identification in information systems: Management challenges and public policy issues. Information Technology @ People, 7(4):6-37, 1994.
  • A. Ross, D. Nandakumar, A.K. Jain, Handbook of Multibiometrics, . Springer, Heidelberg (2006).
  • A. Ross and A.K. Jain, "Information Fusion in Biometrics", Pattern Recognition Letters, Vol 24, Issue 13, pp. 2115-2125, 2003.
  • A. K. Jain, A. Ross, "Multibiometric Systems", Appeared in Communication of the ACM, Special Issue on Multimodal Interfaces, Vol.47, No.1, pp.34-40, January 2004.
  • B. Scholkopf and A. Smola, Learning with Kernels – Support Vector Machines, Regularization, Optimization and Beyond, MIT Press Series, 2002.
  • J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, 2004.
  • B. Gutschoven and P. Verlinde. Multi-modal identity verification using support vector machines (SVM). In International Conference on Information Fusion, volume 2, pages THB3/3–THB3/8, 2000.
  • M. E. Tipping. Sparse bayesian learning and the relevance vector machine. The Journal of Machine Learning Research, 1:211–244, 2001.
  • X. Xiang-min, M. Yun-feng, X. Jia-ni, and Z. Feng-le. Classification performance comparison between RVMand SVM. In IEEE International Workshop on Anti-counterfeiting, Security, Identification, pages 208–211, 2007.
  • F. Zernike. Physica. 1934
  • Yu-Chiang Wang and David Casasent, "Multimodal Biometric Fusion Using Data Quality Information", Proceedings of International Society of Photo-Optical Instrumentation Engineers (SPIE), Vol.5816, 329(2005).
  • G.Zhao and M.Pietikainen, "Dynamic Texture Recognition using Local Binary Patterns with an Application to Facial Expressions,"Pattern Analysis and Machine Intelligence, 29(6),915-928,2007.
  • Jun Zhou, Guangda Su, Chunhong Jiang, Yafeng Deng, and Congcong Li,"A face and fingerprint identity authentication system based on multi-route detection", Neurocomputing, Elsevier Science Publishers, Vol 70, Issue 4-6, pp.922-931, 2007.
  • Djamel Bouchaffra, Abbes Amira, "Structural hidden Markov models for biometrics: Fusion of face and Fingerprint", Pattern Recognition 41 (2008) 852-867.
  • Aloysius George," Bizarre Approaches for Multimodal Biometrics", IJCSNS International Journal of Computer Science and Network Security, vol 8 No 7, July 2008.
  • B.Prasana Lakshmi & A.Kanammal, "Secured Authentication of Space Spacified Token with Biometric Traits – Face and Fingerprint", IJCSNS International Journal of Computer Science and Network Security, vol 9 No 7, July 2009.
  • T.B.Long, L.H.Thai, T.Hanh, "Multimodal Biometric Person Authentication Using Finerprint, Face Features", PRICAI 2012: Trends in Artificial Intelligence, Lecture Notes in Computer Science vol.7458, pp.613-624, 2012.
  • Shan Du and Rabab Ward, "Wavelet based illumination normalization for face recognition". Department of Electrical and Computer Engineering, The University of British Coloumbia, Vancouver,BC , Canada, "IEEE Transactions on pattern Analysis and Machine Intelligence, 0-7803-9134-9. 2005.
  • Belhumeur, P.N., Hespanha, J.P. and Kriegman, D.J.,"Eigenfaces va.Fisherfaces: Recognition using class specific linear projection," IEEE Transactions on pattern Analysis and Machine Intelligence, vol.19, no.7,pp.711-720, 1997.
  • Cootes, T., Taylor, C., Cooper, D. and Graham, J., "Active shape models-their training and applications ,"Computer Vision and Image Understanding, vol.61,no1pp.38-59, 1995.
  • Cootes, T., Edwards, G. and Taylor, C., "Active appearance models," IEEE Transactions on pattern Analysis and Machine Intelligence, vol.23,no.6pp.681-685, 2001.
  • R. Mukundan, S.H. Ong and P.A. Lee, ''Image analysis by Tchebichef moments,'' IEEE Transactions on Image Processing, vol.10, no.9, pp. 1357-1364, 2001.
  • P.T. Yap, R. Paramesran and S.H. Ong, ''Image analysis by Krawtchouk moments,'' IEEE Transactions on Image Processing, vol.12, no.11, pp.1367-1377, 2003.
  • J. C. MacKay. The evidence framework applied to classification networks. Neural Computation, 4(5):720–736, 1992.
  • FVC (2004). Finger print verification contest 2004. In Available at (http://bias.csr.unibo.it/fvc2004.html).
  • ORL, 1992. The ORL face database at the AT&T (Olivetti) Research Laboratory, available online: /http://www.uk.research.att.com/facedatabase.html.
  • Seyed Mehdi Lajevardi, Zahir M.Hussain,"Zernike Moments for Facial Expression Recognition", Internation Conference on Communication, Computer and Power (ICCCP'09), pp.378-381, Muscat, February 15-18, 2009.
  • Hasan Abdel Qader, Abdul Rahman Ramli, and Syed Al-Haddad,"Fingerprint Recognition Using Zernike Moments", The Internation Arab Journal of Information Technology, Vol.4, No.4, Octorber 2007.
Еще
Статья научная
SciUp 2024 © 2024 ©