摘要
提出了一种基于分块重排向量二维局部保持鉴别(Mv2DLPP)的人脸识别方法.先对原始图像矩阵进行分块,重排连接每个子块,每个子块转换成向量,通过列向量表示每个子块,可以得到二维图像,然后对二维矩阵实施2DLPP方法进行特征提取,该方法能有效地提取图像的局部特征.在ORL和YALE人脸库的测试结果验证了该算法的有效性.
In this paper, a modularly vectorized 2DLPP（Mv2DLPP） is proposed for face recognition. First, the original images are divided into modular blocks. Then, each sub-block is transformed into a vector. By using column vector to represent each modular block, we can obtain a two dimensional matrix representation for the image.Finally 2DLPP is applied directly on these 2D matrices. Experimental results on ORL and Yale databases show that the proposed method can achieve better recognition performance in comparison with 2DLPP.
作者
乎西旦.居马洪
古丽娜孜.艾力木江
Huxidan Jumahong;Gulnaz Alimjang(College of Electronic and Information Engineering,Yili Normal University,Yining,Xinjiang 835000,China)
出处
《伊犁师范学院学报（自然科学版）》
2018年第2期68-72,共5页
Journal of Yili Normal University：Natural Science Edition
基金
新疆高校重点基金资助项目(XJEDU2014I043)
国家自然科学基金项目(61363066)
关键词
人脸识别
特征抽取
保局投影
二维保局投影
Face recognition
Feature extraction
locality preserving projection
Two dimensional locality preserving projection
