摘要
主成分分析法(Principal Component Analysis,PCA)是用特征向量对样本数据进行分析,从而达到降维目的的一种多元统计分析方法。为解决PCA方法用于人脸识别时图像维数高、计算量大的问题,采用了新的特征值分解法并在图像预处理阶段加入了滤波处理。在MATLAB平台上搭建了人脸识别系统,对普通PCA方法和加入滤波预处理的PCA方法进行了比较分析,实验证明了加入滤波处理的系统在性能上具有一定的优越性,对实际应用有着一定的参考价值.
Principal Component Analysis(PCA)is a multivariate statistical analysis method which uses feature vectors to analyze sample data and reduce the high-dimension of the feature vectors.In order to solve the problem of high image dimension and large amount of direct calculation when PCA method is used for face recognition,a new feature value decomposition method is adopted and the filter is used to remove the noise of the original image.The face recognition system was built on MATLAB platform,and the common PCA method and the PCA method with filtering pretreatment were compared and analyzed.The experiment proved that the system with filtering processing has certain advantages in performance and certain reference value practical application.
作者
李梦潇
姚仕元
LI Meng-xiao;YAO Shi-yuan(Electric Engineering and Information Department,Sowthwest Petroleum University,Chengdu 610500,China;Oil and Gas Automation Lab,Chengdu 610500,China)
出处
《计算机科学》
CSCD
北大核心
2019年第B06期577-579,共3页
Computer Science
关键词
PCA
特征值分解
人脸识别
滤波
PCA
Eigenvalue decomposition
Face recognition
Filtering
