Abstract
A human face is a complex object with features that can vary over time. Face recognition systems have been investigated while developing biometrics technologies. This paper presents a face recognition system that uses eyes, nose and mouth approximations for training a neural network to recognize faces in different expressions such as natural, smiley, sad and surprised. The developed system is implemented using our face database and the ORL face database. A comparison will be drawn between our method and two other face recognition methods; namely PCA and LDA. Experimental results suggest that our method performs well and provides a fast, efficient system for recognizing faces with different expressions.
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References
Brunelli, R., Poggio, T.: Face Recognition: Features versus Templates. IEEE Trans. PAMI 15, 1042–1052 (1993)
Turk, M., Pentland, A.: Eignefaces for Recognition. Journal of Cognitive Neuroscience 3, 72–86 (1991)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: Face Recognition using class specific linear projection. In: Proc. ECCV, pp. 45–58 (1996)
Vasilescu, M.A.O., Terzopoulos, D.: Multilinear Image Analysis for Facial Recognition. In: Proceedings of International Conference on Pattern Recognition (2002)
Savvides, M., Vijaya Kumar, B.V.K., Khosla, P.K.: Eigenphases vs. Eigenfaces. In: 17th International Conference on Pattern Recognition, vol. 3, pp. 810–813 (2004)
Shen, L., Bai, L.: Gabor Wavelets and Kernel Direct Discriminant Analysis for Face Recognition. In: 17th International Conference on Pattern Recognition, vol. 1, pp. 284–287 (2004)
Dai, G., Qian, Y., Jia, S.: A Kernel Fractional-Step Nonlinear Discriminant Analysis for Pattern Recognition. In: 17th International Conference on Pattern Recognition, vol. 2, pp. 431–434 (2004)
Zhao, S., Grigat, R.: Multiblock-Fusion Scheme for Face Recognition. In: 17th International Conference on Pattern Recognition, vol. 1, pp. 309–312 (2004)
Ahonen, T., Pietikainen, M., Hadid, A.: Face Recognition Based on the Appearance of Local Regions. In: 17th International Conference on Pattern Recognition, vol. 3, pp. 153–156 (2004)
Zhang, B., Zhang, H., Ge, S.: Face recognition by applying wavelet subband representation and kernel associative memory. IEEE Transactions on Neural Networks 15, 166–177 (2004)
Fan, X., Verma, B.: A Comparative Experimental Analysis of Separate and Combined Facial Features for GA-ANN based Technique. In: Proceedings of International Conference on Computational Intelligence and Multimedia Applications, pp. 279–284 (2005)
Khashman, A.: Face Recognition Using Neural Networks and Pattern Averaging. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 98–103. Springer, Heidelberg (2006)
Cambridge University, Olivetti Research Laboratory face database, http://www.uk.research.att.com/facedatabase.html
Lu, X., Wang, Y., Jain, A.K.: Combining Classifiers for Face Recognition. In: IEEE International Conference on Multimedia & Expo (ICME 2003), vol. III, pp. 13–16 (2003)
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Khashman, A., Garad, A.A. (2006). Multi-expression Face Recognition Using Neural Networks and Feature Approximation. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_34
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DOI: https://doi.org/10.1007/11875604_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45764-0
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