A Kernel-Based Nonlinear Representor with Application to Eigenface Classification

被引：6

作者：

张晶

刘本永

谭浩

机构：

[1] UESTC Chengdu 610054 China

[2] School of Electronic Engineering

[3] The Information Center of Sichuan Radio and Television University Chengdu 610073 China School of Computer Science and Technology

来源：

Journal of Electronic Science and Technology | 2004年 / 02期

关键词：

kernel based nonlinear representor; face recognition; eigenfaces; Gaussian kernel; euclidean distance classifier;

D O I：

暂无

中图分类号：

TP391.4 [模式识别与装置];

学科分类号：

0811 ; 081101 ; 081104 ; 1405 ;

摘要：

This paper presents a classifier named kernel-based nonlinear representor (KNR) for optimal representation of pattern features. Adopting the Gaussian kernel, with the kernel width adaptively estimated by a simple technique, it is applied to eigenface classification. Experimental results on the ORL face database show that it improves performance by around 6 points, in classification rate, over the Euclidean distance classifier.

引用

页码：19 / 22

页数：4

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