Abstract
Recently, wavelet transform and image fusion mechanism have been used in face recognition to improve the performance. In this paper, we propose a new face recognition method based on wavelet transform and radial basis function (RBF) fusion network. Firstly, an image is decomposed with wavelet transform (WT) to three levels. Secondly, the Fisherface method is applied to three low-frequency sub-images respectively. Then, the individual classifiers are fused using the RBF neural network. Experimental results show that the proposed method outperforms both individual classifiers and the direct Fisherface method.
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References
Turk, M.A., Pentland, A.P.: Eigenfaces for Recognition. J. Cogn. Neurosci. 3, 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Anal. Machine Intell. 19, 711–720 (1997)
Kwak, K.-C., Pedrycz, W.: Face Recognition Using Fuzzy Integral and Wavelet Decomposition Method. IEEE Trans System, Man, and Cybernetics-B 34, 1666–1675 (2004)
Lai, J.H., Yuen, P.C., Feng, G.C.: Face Recognition Using Holistic Fourier Invariant Features. Pattern Recognition 34, 95–109 (2002)
Chien, J.T., Wu, C.C.: Discriminant Wavelet Faces and Nearest Feature Classifiers for Face Recognition. IEEE Trans. Pattern Anal. Machine Intell. 24, 1644–1649 (2002)
Sugeno, M.: Fuzzy Measures and Fuzzy Integrals—A Survey. In: Gupta, M.M., Saridis, G.N., Gaines, B.R. (eds.) Fuzzy Automata and Decision Processes, pp. 89–102. North Holland, Amsterdam (1977)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall International, Inc., New Jerey (1999)
Pedrycz, W.: Computational Intelligence: An Introduction. CRC Press, New York (1998)
FERET face database, http://www.itl.nist.gov/iad/humanid/feret/
Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Networks 2, 302–309 (1991)
Moller-Levet, C.S., Yin, H.: Modelling and Clustering of Gene Expressions Using Rbfs and a Shape Similarity Metric. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 1–11. Springer, Heidelberg (2004)
Mallat, S.: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989)
Daubechies, I.: Orthonormal Bases of Compactly Supported Wavelets. Commun Pure Appl. Math. 41, 909–996 (1988)
Cohen, A., Daubechies, I., Feauveau, J.: Biorthogonal Bases of Compactly Supported Wavelets. Commun. Pure Appl. Math. XLV, 485–560 (1992)
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Li, B., Yin, H. (2005). Face Recognition Using RBF Neural Networks and Wavelet Transform. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_18
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DOI: https://doi.org/10.1007/11427445_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
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