Abstract
Automatic face recognition, though being a hard problem, has a wide variety of applications. Support vector machine (SVM), to which model selection plays a key role, is a powerful technique for pattern recognition problems. Recently lots of researches have been done on face recognition by SVMs and satisfying results have been reported. However, as SVMs model selection details were not given, those results might have been overestimated. In this paper, we propose a general framework for investigating automatic face recognition by SVMs, with which different model selection algorithms as well as other important issues can be explored. Preliminary experimental results on the ORL face database show that, with the proposed hybrid model selection algorithm, appropriate SVMs models can be obtained with satisfying recognition performance.
This work is supported by the National Natural Science Foundation of China (No. 60072029).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
http://www.viisage.com/ww/en/pub/viisage___products/viisage___products___fac- etools.htm
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A Literature Survey. ACM Computing Surveys 35, 399–458 (2003)
Dai, G., Zhou, C.: Face Recognition Using Support Vector Machines with the Robust Feature. In: Proc. of IEEE workshop on Robot and Human Interactive Communication, pp. 49–53 (2003)
Déniz, O., Castrillón, M., Hernández, M.: Face Recognition Using Independent Component Analysis and Support Vector Machines. Pattern Recognition Letters 24, 2153–2157 (2003)
Li, Z., Tang, S.: Face Recognition Using Improved Pairwise Coupling Support Vector Machines. In: Proc. of Intl. Conf. on Neural Information Processing, vol. 2, pp. 876–880 (2002)
Jonsson, K., Matas, J., Kittler, J., Li, Y.P.: Learning Support Vector Machines for Face Verification and Recognition. In: Proc. of IEEE Intl. Conf. on Automatic Face and Gesture Recognition, pp. 208–213 (2000)
Guo, G., Li, S., Kapluk, C.: Face Recognition by Support Vector Machines. In: Proc. of IEEE Intl. Conf. on Automatic Face and Gesture Recognition, pp. 196–201 (2000)
Kressel, U.: Pairwise Classification and Support Vector Machines. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods: Support Vector Learning, pp. 255–268. MIT Press, Cambridge (1999)
Hsu, C.-W., Lin, C.-J.: A Comparison of Methods for Multi-Class Support Vector Machines. IEEE Trans. on Neural Networks 13, 415–425 (2002)
Burges, C.J.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2, 121–267 (1998)
Chung, K.-M., Kao, W.-C., Sun, T., Wang, L.-L., Lin, C.-J.: Radius Margin Bounds for Support Vector Machines with the RBF Kernel. Neural Computation 11, 2643–2681 (2003)
Chang, C.-C., Lin, C.-J.: LIBSVM: A Library for Support Vector Machines (2002), Online at http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf
Staelin, C.: Parameter Selection for Support Vector Machines (2003), Online at http://www.hpl.hp.com/techreports/2002/HPL-2002-354R1.pdf
Li, H.-Q., Wang, S.-Y., Qi, F.-H.: Minimal Enclosing Sphere Estimation and Its Application to SVMs Model Selection. In: Proc. of IEEE Intl. Sympo. on Neural Networks, pp. 487–493 (2004)
Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing Multiple Parameters for Support Vector Machines. Machine Learning 46, 131–159 (2002)
Keerthi, S.S.: Efficient Tuning of SVM Hyperparameters Using Radius/Margin Bound and Iterative Algorithms. IEEE Trans. on Neural Networks 13, 1225–1229 (2002)
Chung, K.-M., Kao, W.-C., Sun, T., Wang, L.-L., Lin, C.-J.: Radius Margin Bound for Support Vector Machines with the RBF Kernel. In: Wang, L., Rajapakse, J.C., Fukushima, K., Lee, S.Y., Yao, X. (eds.) Proc. of the 9th Intl. Conf. on Neural Information Processing, vol. 2, pp. 893–897 (2002)
Vapnik, V.: Statistical Learning Theory. Wiley, Chichester (1998)
Friedman, J.: Another approach to polychotomous classification. Technical report, Stanford University (1996)
Platt, J.: Probabilistic Outputs for Support Vector Machines and Comparison to Regularized Likelihood Methods. In: Smola, A.J., Bartlett, P.L., Schölkopf, B., Schuurmans, D. (eds.) Advances in Large Margin Classifiers, pp. 61–74. MIT Press, Cambridge (2000)
Wu, T.-F., Lin, C.-J., Weng, R.C.: Probability Estimates for Multi-Class Classification by Pairwise Coupling. Journal of Machine Learning Research 5, 975–1005 (2004)
Goh, K., Chang, E., Cheng, T.: Support Vector Machine Pairwise Classifiers with Error Reduction for Image Classification. In: Proc. of ACM Intl. Conf. on Multimedia (MIR Workshop), pp. 32–37 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, H., Wang, S., Qi, F. (2004). Automatic Face Recognition by Support Vector Machines. In: Klette, R., Žunić, J. (eds) Combinatorial Image Analysis. IWCIA 2004. Lecture Notes in Computer Science, vol 3322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30503-3_55
Download citation
DOI: https://doi.org/10.1007/978-3-540-30503-3_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23942-0
Online ISBN: 978-3-540-30503-3
eBook Packages: Computer ScienceComputer Science (R0)