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基于局部线性嵌入与主成分分析的人脸识别方法
引用本文:熊明,王汝言,唐琳.基于局部线性嵌入与主成分分析的人脸识别方法[J].重庆邮电学院学报(自然科学版),2009(1).
作者姓名:熊明  王汝言  唐琳
作者单位:重庆邮电大学通信与信息工程学院;中国电信股份有限公司珠海分公司;
基金项目:国家863计划项目(2005AA122310);;重庆市教委项目(KJ070511);;教育部新世纪优秀人才支持计划;;重邮博士启动基金(A2007-60)
摘    要:针对主成分分析(PCA)算法对数据进行向量化,破坏初始数据的局部结构信息的缺点,提出了将局部线性嵌入(LLE)与PCA相结合的人脸识别算法。先采用LLE提取的初始数据保留了人脸局部结构信息的低维特征,再利用PCA计算低维数据的主要成分,最后根据各人脸的主要成分之间的欧式距离判断是否匹配。对比实验表明,该算法在明显提升算法效率的同时,保证了较高的识别率。

关 键 词:LLE  PCA  人脸识别  识别率  

Face recognition based on locally linear embedding and principal component analysis
XIONG Ming,WANG Ru-yan,TANG Lin.Face recognition based on locally linear embedding and principal component analysis[J].Journal of Chongqing University of Posts and Telecommunications(Natural Sciences Edition),2009(1).
Authors:XIONG Ming  WANG Ru-yan  TANG Lin
Institution:1.School of Communication and Information Engineering;Chongqing University of Posts and Telecommunications;Chongqing 400065;P.R.China;2.Zhuhai Branch of China Telecom Limited Company;Zhuhai 519002;P.R.China
Abstract:Focusing on the disadvantage that the principal component analysis(PCA)algorithm destroy the primary data of local structural information when it vectors the data,a face recognition algorithm that combines the locally linear embedding(LLE)with PCA was proposed.First,the low-dimensional features from the initial data which preserving the local structure information of face image was extracted by LLE.Secondly,the main components of the low-dimensional data with PCA were calculated.At last,the main components ...
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