基于PCA的人脸识别系统的设计与改进

计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 577-579.

基于PCA的人脸识别系统的设计与改进

李梦潇¹, 姚仕元^1,2

西南石油大学电气信息学院成都610500¹;
油气自动化实验室成都610500²

出版日期:2019-06-14 发布日期:2019-07-02
通讯作者: 李梦潇(1995-),女,硕士,主要研究方向为罗克韦尔实验室设备管理系统设计,E-mail:1785359937@qq.com
作者简介:姚仕元(1994-),男,硕士,主要研究方向为交流充电桩关键技术,E-mail:243402563@qq.com。

Design and Improvement of Face Recognition System Based on PCA

LI Meng-xiao¹, YAO Shi-yuan^1,2

Electric Engineering and Information Department,Sowthwest Petroleum University,Chengdu 610500,China¹;
Oil and Gas Automation Lab,Chengdu 610500,China²

Online:2019-06-14 Published:2019-07-02

摘要/Abstract

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

关键词: PCA, 滤波, 人脸识别, 特征值分解

Abstract: Principal Component Analysis (PCA) is a multivariate statistical analysis method whichuses 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 andcertain reference value practical application.

Key words: Eigenvalue decomposition, Face recognition, Filtering, PCA

中图分类号:

TP391.4

李梦潇, 姚仕元. 基于PCA的人脸识别系统的设计与改进[J]. 计算机科学, 2019, 46(6A): 577-579. https://doi.org/

LI Meng-xiao, YAO Shi-yuan. Design and Improvement of Face Recognition System Based on PCA[J]. Computer Science, 2019, 46(6A): 577-579. https://doi.org/

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