Computer Recognition Systems 3 pp 95-102
The Comparison of Normal Bayes and SVM Classifiers in the Context of Face Shape Recognition
In this paper the face recognition system based on the shape information extracted with the Active Shape Model is presented. Three different classification approaches have been used: the Normal Bayes Classifier, the Type 1 Linear Support Vector Machine (LSVM) and the type 2 LSVM with a soft margin. The influence of the shape extraction algorithm parameters on the classification efficiency has been investigated. The experiments were conducted on a set of 3300 images of 100 people which ensures the statistical significance of the obtained results.
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- 1.Cootes, T., Cooper, D., Taylor, C., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)Google Scholar
- 2.Cootes, T., Taylor, C.: Statistical models of appearance for computer vision. Technical report, University of Manchester, Wolfson Image Analysis Unit, Imaging Science and Biomedical Engineering (2004)Google Scholar
- 3.Zhao, M., Li, S., Chen, C., Bu, J.: Shape Evaluation for Weighted Active Shape Models. In: Proc. of the Asian Conference on Computer Vision, pp. 1074–1079 (2004)Google Scholar
- 4.Zuo, F., de With, P.: Fast facial feature extraction using a deformable shape model with haar-wavelet based local texture attributes. In: Proc. of ICIP 2004, pp. 1425–1428 (2004)Google Scholar
- 5.Ge, X., Yang, J., Zheng, Z., Li, F.: Multi-view based face chin contour extraction. Engineering Applications of Artificial Intelligence 19, 545–555 (2006)Google Scholar
- 6.Wan, K.-W., Lam, K.-M., Ng, K.-C.: An accurate active shape model for facial feature extraction. Pattern Recognition Letters 26(15), 2409–2423 (2006)Google Scholar
- 7.Cristinacce, D., Cootes, T.: Boosted Regression Active Shape Models. In: Proc. British Machine Vision Conference 2007, vol. 2, pp. 880–889 (2007)Google Scholar
- 8.Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Elsevier/Academic Press, Amsterdam (2003)Google Scholar
- 9.Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley & Sons, Chichester (2001)Google Scholar
- 10.Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Berlin (2000)Google Scholar
- 11.Burges, C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)Google Scholar