论文信息 - Face recognition method based on Within-class Clustering SVM

Face recognition method based on Within-class Clustering SVM

A face recognition method based on within-class Clustering SVM (CCSVM) is presented in this paper in order to decrease the negative effect caused by noisy training samples in the recognition process. Based on the discontinuity of finite samples distribution in the high dimension space and the existence of noisy samples, first, we re-cluster samples within the class, find out the cluster centres to form the virtual classes, and then divide virtual classes of all the classes by SVM. Experiment results show that this method follows the distribution law of points in high-dimensional space and can achieve better performance than some traditional methods.

Ying Xia | Yan Wu | Xiao Yao

[1] Sudipto Guha,et al. CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.

[2] L. Saul,et al. An Introduction to Locally Linear Embedding , 2001 .

[3] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[4] Peter J. Rousseeuw,et al. Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[5] Dimitrios Gunopulos,et al. Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.

[6] Ulrich H.-G. Kreßel,et al. Pairwise classification and support vector machines , 1999 .

[7] Chih-Jen Lin,et al. A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[8] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[9] Federico Girosi,et al. Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10] Ali S. Hadi,et al. Finding Groups in Data: An Introduction to Chster Analysis , 1991 .