Abstract
In this paper we propose a two-pass classification method and apply it to face recognitions. The method is obtained by integrating together two approaches, the hyper-ellipsoid neural networks (HENN’s) and the SVM’s with error correcting codes. This method realizes a classification operation in two passes: the first one is to get an intermediate classification result for an input sample by using the HENN’s, and the second pass is followed by using the SVM’s to re-classify the sample based on both the input data and the intermediate result. Simulations conducted in the paper for applications to face recognition showed that the two-pass method can maintain the advantages of both the HENN’s and the SVM’s while remedying their disadvantages. Compared with the HENN’s and the SVM’s, a significant improvement of recognition performance over them has been achieved by the new method.
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References
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Surveys 35(4), 399–458 (2003)
Chellappa, R., Wilson, C.L., Sirohey, S.: Human and Machine Recognition of Faces: A Survey. Proceedings of the IEEE 83(5), 705–741 (1995)
Vapnik, V.: Statistical Learning Theory. John Willey and Sons Inc., New York (1998)
Sebald, D.J., Bucklew, J.A.: Support Vector Machines and Multiple Hypothesis Test Problem. IEEE Trans. on Signal Processing 49(11), 2865–2872 (2001)
Kreßel, U.: Pairwise Classification and Support Vector Machines. In: Schölkopf, B., Burges, J.C., Smola, A.J. (eds.) Advances in Kernel Methods: Support Vector Learning, pp. 255–268. MIT Press, Cambridge (1999)
Dietterich, T., Bakiri, G.: Solving Multiclass Learning Problems via Error-Correcting Output Codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)
Wang, C.B., Guo, C.A.: An SVM Classification Algorithm with Error Correction Ability Applied to Face Recognition. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 1057–1062. Springer, Heidelberg (2006)
Wang, S.J., Chen, X.: Biomimetic (Topological) Pattern Recognition – a New Model of Pattern Recognition Theory and Its Application. In: Proceedings of the International Joint Conference on Neural Networks, vol. 3, pp. 2258–2262 (2003)
Shou-jue, W., Xu, C., Hong, Q., Weijun, L., Yi, B.: Double Synaptic Weight Neuron Theory and Its Application. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 264–272. Springer, Heidelberg (2005)
Lin, S., Costello, D.J.: Error Control Coding: Fundamentals and Applications. Prentice-Hall, Inc., Englewood Cliffs (1983)
Turk, M.A., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuro-Science 3(1), 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenface vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. on PAMI 19(7), 711–720 (1997)
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Guo, C., Yuan, C., Ma, H. (2007). A Two-Pass Classification Method Based on Hyper-Ellipsoid Neural Networks and SVM’s with Applications to Face Recognition. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_59
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DOI: https://doi.org/10.1007/978-3-540-72395-0_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
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