Face Recognition Using Direct-Weighted LDA
Face Recognition Using Direct-Weighted LDA
复制标题
使用直接加权 LDA 进行人脸识别
DOI：
10.1007/978-3-540-28633-2_80
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Xin Yang
中科院分区：
文献类型：
--
作者：
Dake Zhou;Xin Yang
本文提出了一种直接加权LDA（DW-LDA）人脸识别方法，它可以有效地解决基于LDA的人脸识别方法中存在的两个问题：1）Fisher准则在分类率方面的非最优性; 2）“小样本”问题。特别地，DW-LDA方法还可以通过使用合适的加权方案来提高一个或多个指定类的分类率。该方法首先通过丢弃类间散布矩阵的零空间来降低原始输入空间的维数，该零空间不包含显著的判别信息。在降维子空间中利用加权格式重构类内和类间散布矩阵后，用全类散布矩阵代替传统Fisher准则中的类内散布矩阵，得到了一种改进的Fisher准则. LDA使用修改后的标准，然后实施，以找到低维的功能与显着的歧视的权力。在ORL和Yale人脸库上的实验表明，该方法是一种有效的人脸识别方法。
This paper introduces a direct-weighted LDA (DW-LDA) approach to face recognition, which can effectively deal with the two problems encountered in LDA-based face recognition approaches: 1) Fisher criterion is nonoptimal with respect to classification rate, and 2) the ”small sample size” problem. In particular, the DW-LDA approach can also improve the classification rate of one or several appointed classes by using a suitable weighted scheme. The proposed approach first lower the dimensionality of the original input space by discarding the null space of the between-class scatter matrix containing no significant discriminatory information. After reconstructing the between- and within-class scatter matrices in the dimension reduced subspace by using weighted schemes, a modified Fisher criterion is obtained by replacing the within-class scatter matrix in the traditional Fisher criterion with the total-class scatter matrix. LDA using the modified criterion is then implemented to find lower-dimensional features with significant discrimination power. Experiments on ORL and Yale face databases show that the proposed approach is an efficient approach to face recognition.
基于特征脸的人脸建模与识别
DOI：
--
发表时间：
2003
期刊：
IPSJ SIG Technical Reports Vol. CVIM-139
影响因子：
--
作者：
T.;Shakunaga;F.;Sakaue;Y.;Matsubara
通讯作者：
Matsubara