摘要
二维投影利用表示图像的矩阵直接抽取特征．计算量主要与图像的大小有关，能适用于大类别的人脸识别。针对二维投影抽取出的特征是矩阵，存在特征之间的冗余度大、特征数量多、不利于存储和分类等弱点，该文通过二维投影后的样本再作一次向量形式的特征抽取办法进一步降低二维投影抽取出的特征数量，并缩短了特征识别时间。计算机仿真研究验证了所提出方法的正确性。
Two-dimension LDA （2DLDA） and other two-dimensional projection methods can directly extract features by using original image matrixes. But the features extracted by two-dimensional approaches are still matrixes; it could cause the magnitude of features too much and slow down the classification speed. One new algorithms are used to compress the feature matrixes in this paper. The method combined the virtues of two-dimension method and one-dimension method. Which use one-dimension method to compress the feature matrixes after extracted by 2DLDA.
作者
张博
ZHANG Bo （College of Electrical Engineering,Liaoning University of Technology,Jinzhou 121001 ,China）
出处
《电脑知识与技术》
2009年第1期186-188,共3页
Computer Knowledge and Technology
关键词
人脸识别
特征抽取
主分量分析
二维投影分析
face recognition
feature extraction
principal component analysis（PCA）
two dimensional projection analysis
