Using Nearest Neighbor Rule to Improve Performance of Multi-Class SVMs for Face Recognition(Multimedia Systems)

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概要

• 論文の詳細を見る

• The classification time required by conventional multi-class 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.

• 社団法人電子情報通信学会の論文
• 2004-04-01

著者

• Park Jong-wook

  Department Of Electronic Engineering University Of Incheon

• Park Sung-wook

  Department Of Electronic Engineering University Of Incheon

• Park Sung-wook

  Department Of Dermatology Busan Paik Foundation Hospital Inje University Medical College

関連論文

• Using Nearest Neighbor Rule to Improve Performance of Multi-Class SVMs for Face Recognition(Multimedia Systems)
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