Improving the Performance of Multi-Class SVMs in Face Recognition with Nearest Neighbor Rule

Authors:
Chang-Hun Lee;Sung-Wook Park;Weide Chang;Jong-Wook Park
Affiliations:
-;-;-;-
Venue:
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Year:
2003

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Abstract

The classification time required by conventional multiclass SVMs greatly increases as the number of pattern classes increases. This is due to the fact that the needed set of binary class SVMs gets quite large. In this paper, we propose a method to reduce the number of classes by using nearest neighbor rule (NNR) in the principle component analysis and linear discriminant analysis (PCA+LDA) feature subspace. The proposed method reduces the number of face classes by selecting a few classes closest to the test data projected in the PCA+LDA feature subspace. Results of experiment show that our proposed method has a lower error rate than nearest neighbor classification (NNC) method. Though our error rate is comparable to the conventional multi-class SVMs, the classification process of our method is much faster.