摘要
提出了一种融合全局和局部特征的Fisherfaces方法。在Fisher线性准则下,抽取出图像全局特征和局部特征的最佳分类特征。计算待识别样本和训练样本集的加权欧氏距离。在最近邻准则下,判别待识别样本的类别,在ORL人脸库上进行的对比实验结果表明该方法的优越性。
The face recognition is an active subject in the field of computer vision and pattern recognition,which has a wide range of potential applications.In this paper,the Fisheffaces method based on the fusion of global and local facial features is presented.Fisher linear discriminating analysis is performed to＂ extract the most discriminating features.Both local and global features＇ excellence is fused in the method.The experiments on the ORL database demonstrate the effectiveness and feasibility of the presented method.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第24期194-196,211,共4页
Computer Engineering and Applications
基金
国家自然科学基金No.60673092
教育部科学技术研究重点项目No.205059
江苏省高技术研究计划项目(No.BG2005019)~~
关键词
人脸识别
主成分分析
全局特征
局部特征
Fisher线性准则
最佳分类特征
face recognition
principle component analysis
local features
global features
Fisher linear rule
the most discriminating features
