Two-Stage Data Reduction for a SVM Classifier in a Face Recognition Algorithm Based on the Active Shape Model
In this paper, two stage data reduction method for face identification with use of Support Vector Machine (SVM) classifier is evaluated. SVM Classification was performed for data acquired from contour description of 2200 faces of 100 persons. Face contours were extracted from frontal face images with use of Active Shape Model (ASM) method. Two stage PCA+LDA data reduction performance is measured in comparison with single stage PCA or LDA reductions. We propose to replace first stage PCA reduction with much simpler and less computationally intensive contour decimation.
KeywordsSupport Vector Machine Active Shape Model Support Vector Machine Training Support Vector Machine Kernel Face Contour
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- 2.Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
- 4.Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding (61), 38–59 (1995)Google Scholar
- 5.Cootes, T.F., Taylor, C.J.: Statistical Models of Appearance for Computer Vision. Technical report, Imaging Science and Biomedical Engineering, University of Manchester (2001)Google Scholar
- 6.Vapnik, V., Cortes, C.: Support-Vector Networks. Machine Learning (20) (1995)Google Scholar
- 7.Kasinski, A., Florek, A., Schmidt, A.: The PUT face database. Image Processing and Communications 13(3-4), 59–64 (2008)Google Scholar