ICCSA 2004: Computational Science and Its Applications – ICCSA 2004 pp 828-837 | Cite as
Multi-modal Biometircs System Using Face and Signature
Abstract
In this paper, we propose a multi-modal biometrics system based on the face and signature recognition. For this, we suggest biometric algorithms for the face and signature recognition. First, we describe a fuzzy linear discriminant analysis (LDA) method for the face recognition. It is an expanded version of the Fisherface method using the fuzzy logic which assigns fuzzy membership to the LDA feature values. On the other hand, the signature recognition has the problem that its performance is often deteriorated by signature variation from various factors. Therefore, we propose a robust online signature recognition method using LDA and so-called Partition Peak Points (PPP) matching technique. Finally, we propose a fusion method for multi-modal biometrics based on the support vector machine. From the various experiments, we find that the proposed method renders higher recognition rates comparing with the single biometric cases under various situations.
Keywords
Support Vector Machine Feature Vector Face Recognition Linear Discriminant Analysis Face ImagePreview
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References
- 1.Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3, 72–86 (1991)CrossRefGoogle Scholar
- 2.Zhao, W., Krishnaswamy, A., Chellappa, R.: Discriminant Analysis of Principal Components for Face Recognition. In: Face Recognition from Theory to Application, Springer, Heidelberg (1998)Google Scholar
- 3.Kiran, G.V., Kunte, R.S.R., Saumel, S.: On-line signature verification system using probabilistic feature modeling. In: Signal Processing and its Applications, Sixth International Symposium, vol. 1, pp. 351–358 (2001)Google Scholar
- 4.Mingming, M.: Acoustic on-line signature verification based on multiple models. In: Computational Intelligence for Financial Engineering (CIFEr) Proceedings of the IEEE/IAFE/INFORMS Conference, pp. 30–33 (2000)Google Scholar
- 5.Kim, H.C., Kim, D., Bang, S.Y.: Face recognition using the mixture-of-eigenface method. Pattern Recognition Letters 23, 1549–1558 (2002)MATHCrossRefGoogle Scholar
- 6.Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)MATHGoogle Scholar
- 7.Ross, A., Jain, A.: Information fusion in biometrics. Pattern Recognition Letters 24, 2115–2125 (2003)CrossRefGoogle Scholar
- 8.Duda, R.O., Hart, P.E., Stock, D.G.: Pattern Classification, 2nd edn. Wiley & Sons, Inc., Chichester (2001)MATHGoogle Scholar
- 9.Cho, S.-B., Kim, J.H.: Multiple Network Fusion Using Fuzzy Logic. IEEE Trans. on Neural networks 6(2) (1995)Google Scholar