摘要
提出了一种新的基于LDA的人脸识别算法。该方法重新定义了样本的类间散布矩阵,在原始的定义基础上增加了一种径向基函数(RBF)调节类间距离,使得在选择投影方向时能更好地分开各类样本;同时该方法在类间散布矩阵与类内散布矩阵的特征分解的基础上,通过变换求出符合Fisher准则的最优投影方向,可以证明这样得到的投影方向同时具有正交性与统计不相关性。通过ORL人脸数据库的数值实验,表明了该算法比传统算法有更好的性能。
This paper introduces a new approach of improved-LDA to overcome the drawbacks existing in the traditional PCA and LDA methods. It redefines the between-class scatter matrix by adding a radical basis function（RBF）. Therefore, it can work better than the traditional methods. At the same time, a optimal set of uncorrelated discriminant vectors have been founded on the basis of the eigen decomposition of between-class scatter matrix and within-class scatter matrix. The numerical experiments on facial database of ORL show this method achieves better performance of face recognition than traditional methods.
出处
《计算机工程》
EI
CAS
CSCD
北大核心
2006年第4期211-213,共3页
Computer Engineering
关键词
线性判别分析
样本类间离散度
样本类内离散度
特征提取
人脸识别
Linear discriminant analysis（LDA）
Between-class scatter
Within-class scatter
Feature extraction
Face recognition
