International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 7, No. 5, 8 May 2015
Cover page and Table of Contents: PDF (size: 633KB)
Person Authentication using Relevance Vector Machine (RVM) for Face and Fingerprint
Full Text (PDF, 633KB), PP.8-15
Views: 0 Downloads: 0
Author(s)
Long B. Tran
1,*
Thai H. Le
1
Index Terms
Multimodal Biometric, Feature Level Fusion, Face, Fingerprint, Recognition System, Relevance vector machine, Zernike moment
Abstract
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).
Cite This Paper
Long B. Tran, Thai H. Le, "Person Authentication using Relevance Vector Machine (RVM) for Face and Fingerprint", International Journal of Modern Education and Computer Science (IJMECS), vol.7, no.5, pp.8-15, 2015. DOI:10.5815/ijmecs.2015.05.02
Reference
[1]J. Campbell Jr., L. Alyea, and J. Dunn. Biometric security: Government application and operations. http://www.vitro.bloomington.in.us:8080/~BC/,1996.
[2]S.G.Davies. Touching big brother: How biometric technology will fuse flesh and machine. Information Technology @ People, 7(4):60-69, 1994.
[3]E. Newham. The Biometric Report. SJB Services, New York, 1995.
[4]K. Jain, L. Hong and S. Pankanti, Biometrics: Promising Frontiers for Emerging Identification Market, Comm. ACM , pp. 91-98, Feb. 2000.
[5]R.Clarke. Human identification in information systems: Management challenges and public policy issues. Information Technology @ People, 7(4):6-37, 1994.
[6]A. Ross, D. Nandakumar, A.K. Jain, Handbook of Multibiometrics, . Springer, Heidelberg (2006).
[7]A. Ross and A.K. Jain, “Information Fusion in Biometrics”, Pattern Recognition Letters, Vol 24, Issue 13, pp. 2115-2125, 2003.
[8]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.
[9]B. Scholkopf and A. Smola, Learning with Kernels – Support Vector Machines, Regularization, Optimization and Beyond, MIT Press Series, 2002.
[10]J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, 2004.
[11]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.
[12]M. E. Tipping. Sparse bayesian learning and the relevance vector machine. The Journal of Machine Learning Research, 1:211–244, 2001.
[13]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.
[14]F. Zernike. Physica. 1934.
[15]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).
[16]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.
[17]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.
[18]Djamel Bouchaffra, Abbes Amira, “Structural hidden Markov models for biometrics: Fusion of face and Fingerprint”, Pattern Recognition 41 (2008) 852-867.
[19]Aloysius George,” Bizarre Approaches for Multimodal Biometrics”, IJCSNS International Journal of Computer Science and Network Security, vol 8 No 7, July 2008.
[20]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.
[21]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.
[22]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.
[23]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.
[24]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.
[25]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.
[26]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.
[27]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.
[28]J. C. MacKay. The evidence framework applied to classification networks. Neural Computation, 4(5):720–736, 1992.
[29]FVC (2004). Finger print verification contest 2004. In Available at (http://bias.csr.unibo.it/fvc2004.html).
[30]ORL, 1992. The ORL face database at the AT&T (Olivetti) Research Laboratory, available online: /http://www.uk.research.att.com/facedatabase.html.
[31]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.
[32]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.