Abstract
This paper proposes a new face recognition approach by using the Discrete Cosine Transform (DCT) and Hierarchical Radial Basis Function Network (HRBF) classification model. The DCT is employed to extract the input features to build a face recognition system, and the HRBF is used to identify the faces. Based on the pre-defined instruction/operator sets, a HRBF model can be created and evolved. This framework allows input features selection. The HRBF structure is developed using Extended Compact Genetic Programming (ECGP) and the parameters are optimized by Differential Evolution (DE). Empirical results indicate that the proposed framework is efficient for face recognition.
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References
Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face Recognition: A literature survey. Technical Report CART-TR-948. University of Maryland (August 2002)
Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: A survey. Proc. IEEE. 83(5), 705–740 (1995)
Valentin, D., Abdi, H., Toole, A.J.O., Cottrell, G.W.: Connectionist models of face processing. A survey, Pattern Recognit 27, 1209–1230 (1994)
Mat Isa, N.A., Mashor, M.Y., Othman, N.H.: Diagnosis of Cervical Cancer using Hierarchical Radial Basis Function (HiRBF) Network. In: Yaacob, S., Nagarajan, R., Chekima, A. (eds.) Proceedings of the International Conference on Artificial Intelligence in Engineering and Technology, pp. 458–463 (2002)
Ferrari, S., Frosio, I., Piuri, V., Alberto Borghese, N.: Automatic Multiscale Meshing Through HRBF Networks. IEEE Trans. on Instrumentation and Measurment 54(4), 1463–1470 (2005)
Ahmad, Z., Zhang, J.: Bayesian selective combination of multiple neural networks for improving long-range predictions in nonlinear process modelling. Neural Comput & Applic. 14, 78C–87C (2005)
Yang, F., Paindavoine, M.: Implementation of an RBF neural network on embedded systems: Real-time face tracking and identity verification. IEEE Trans. Neural Netw. 14(5), 1162–1175 (2003)
Sorwar, G., Abraham, A., Dooley, L.: Texture Classification Based on DCT and Soft Computing. In: The 10th IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2001, vol. 2, pp. 545–548. IEEE Press, Los Alamitos (2001)
Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, International Computer Science Institute, Berkley (1995)
Price, K.: Differential Evolution vs. the Functions of the 2nd ICEO. In: proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC 1997), Indianapolis, USA, pp. 153–157 (1997)
Chen, Y., Yang, B., Dong, J.: Nonlinear System Modeling via Optimal Design of Neural Trees. International Journal of Neural Systems 14, 125–137 (2004)
Chen, Y., Yang, B., Dong, J., Abraham, A.: Time-series Forecasting using Flexible Neural Tree Model. Information Science 174, 219–235 (2005)
Sastry, K., Goldberg, D.E.: Probabilistic model building and competent genetic programming. In: Riolo, R.L., Worzel, B. (eds.) Genetic Programming Theory and Practise, pp. 205–220 (2003)
Su, H., Feng, D., Zhao, R.-C.: Face Recognition Using Multi-feature and Radial Basis Function Network. In: Proc. of the Pan-Sydney Area Workshop on Visual Information Processing (VIP2002), Sydney, Australia, pp. 183–189 (2002)
Huang, R., Pavlovic, V., Metaxas, D.N.: A hybrid face recognition method using markov random fields. In: ICPR 2004, pp. 157–160 (2004)
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Chen, Y., Zhao, Y. (2006). Face Recognition Using DCT and Hierarchical RBF Model. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_43
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DOI: https://doi.org/10.1007/11875581_43
Publisher Name: Springer, Berlin, Heidelberg
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