Face recognition using PCA on enhanced image for single training images

被引：0
|
作者：
He, Jia-Zhong [1 ,2 ]
Zhu, Qing-Huan [1 ]
Du, Ming-Hui [2 ]
机构：
[1] Shaoguan Coll, Sch Informat Engn, Shaoguan 512005, Peoples R China
[2] South China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510640, Peoples R China
来源：
PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2006年
关键词：
face recognition; principal component analysis (PCA); eigenface; discrete cosine transform (DCT);
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An image enhancement based Principal component analysis (PCA) method is proposed to deal with face recognition with single training image per person. The method combines the original training image is with its reconstructed image using only a few low-frequency Discrete Cosine Transform (DCT) coefficients and then performs PCA on the enhanced training images set. In comparison with the standard eigenface algorithm and recent single training image based extended eigenface algorithms on ORL face database, the proposed method shows an improvement of more than 6% in recognition accuracy.
引用
收藏
页码：3218 / +
页数：2
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