Enhanced Discriminant Linear Regression Classification for Face Recognition

被引：0

作者：

Qu, Xiaochao ^{[1
]}

Kim, Hyoung Joong ^{[1
]}

机构：

[1] Korea Univ, Ctr Informat Secur Technol, Seoul 136171, South Korea

来源：

2014 IEEE NINTH INTERNATIONAL CONFERENCE ON INTELLIGENT SENSORS, SENSOR NETWORKS AND INFORMATION PROCESSING (IEEE ISSNIP 2014) | 2014年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Linear Discriminant regression classification (L-DRC) embeds the fisher criterion into the linear regression classification (LRC) and can achieve more robust classification performance for face recognition. In this paper, we propose an enhanced discriminant linear regression classification (EDLRC) algorithm to further improve the discriminant power of LDRC. When calculating the between-class reconstruction error (BCRE), only those classes that are more easily to be misclassified into are considered. After maximizing the ratio of BCRE and within-class reconstruction error (WCRE), the obtained projection matrix in EDERC is more effective than the projection matrix in LDRC, which is verified by extensive experiments.

引用

页数：5

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