计算机技术与发展2012，Vol.22Issue(9)：87-90,4.
一种基于2DLPP和2DLDA的人脸识别方法研究
Face Recognition Based on Feature Fusion by 2DLPP and 2DLDA
摘要
Abstract
Locality preserving projections (LPP) and linear discriminant analysis (LDA) are two effective 1D feature extraction methods, which have been widely applied to face recognition. However, such 1D feature extraction methods always destroy the structure information in a face image when converting it into a vector. And since face images are high-dimensional, 1D methods suffer the singular problem (small sample size problem) when performing eigen-decomposition and inverse computation for the scatter matrices. In this paper, propose a novel feature fusion approach for face recognition, which fuses the features extracted by two-dimensional LPP (2DLPP) and two-dimensional LDA (2DLDA). The experiment based on AR face database shows that this proposed method can perform better results than the traditional LPP and LDA methods.关键词
人脸识别/特征抽取/特征层融合Key words
face recognition/feature extraction/feature level fusion分类
信息技术与安全科学引用本文复制引用
韩璐..一种基于2DLPP和2DLDA的人脸识别方法研究[J].计算机技术与发展,2012,22(9):87-90,4.基金项目
江苏省研究生培养创新工程(CXLX11_0418) （CXLX11_0418）
