Modular approach on Kernel Principal Component Analysis for enhanced Face Recognition

被引：0

作者：

Parvathi, V. S. ^{[1
]}

Satheesh, Smitha ^{[1
,2
]}

Sankaran, Praveen ^{[3
]}

机构：

[1] Coll Engn, Dept Elect & Commun, Trivandrum, Kerala, India

[2] Coll Engn, Dept Elect & Commun, Trivandrum, Kerala, India

[3] NIT Calicut, Dept Elect & Commun, Kerala, India

来源：

2012 ANNUAL IEEE INDIA CONFERENCE (INDICON) | 2012年

关键词：

face recognition; kernel PCA; modular PCA;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A novel face recognition approach, modular kernel principal component analysis (MKPCA), combining the idea of modularity in a kernel method is proposed in this paper. In this technique, face images are divided into sub images (modular approach) and features are extracted from a high dimensional space formed using a Gaussian kernel. This method combines advantages of both modular PCA - more local features and kernel PCA - non linear modelling of data. Simulation results on standard databases show that the proposed MKPCA method of face recognition out performs PCA, modular PCA and kernel PCA in recognition rates.

引用

页码：885 / 890

页数：6

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