基于小波变换和改进PCA的人脸特征提取算法  

Face Feature Extraction Algorithim Based on Wavelet Transform and Improved Principal Component Analysis

作　　者：张颖 马承泽 杨平 王新民 ZHANG Ying;MA Chengze;YANG Ping;WANG Xinmin(College of Information Engineering,Changchun University of Finance and Economics,Changchun 130122,China;College of Mathematics and Statisticsis,Changchun University of Technology,Changchun 130012,China)

机构地区：[1]长春财经学院信息工程学院,长春130122 [2]长春工业大学数学与统计学院,长春130012

出　　处：《吉林大学学报（理学版）》2021年第6期1499-1503,共5页Journal of Jilin University:Science Edition

基　　金：国家自然科学基金(批准号:51278065).

摘　　要：针对在人脸图像高维数据降维时单纯使用主成分分析(PCA)算法的提取精度和速度受限问题,提出一种基于小波变换和改进PCA的混合特征提取算法.该方法首先对人脸图像进行小波分解,选取低频分量对人脸图像进行特征提取;然后利用改进的PCA算法进行主成分提取,获得代表人脸特征的特征向量;最后将该算法应用于Olivetti Faces人脸库数据集的图像分类.实验结果表明,经过该混合算法处理后的图像特征数据,由卷积神经网络(CNN)算法分类识别时准确率提升10%,识别速度提高约37%.Aiming at the problem that extraction accuracy and speed were limited when using principal component analysis(PCA)algorithm only in face image high-dimensional data dimensionality reduction,we proposed a hybrid feature extraction algorithm based on wavelet transform and improved PCA.Firstly,the face image was decomposed by wavelet,and low-frequency component was selected for feature extraction.Secondly,the improved PCA algorithm was used for principal component extraction to obtain the feature vectors representing face features.Finally,the algorithm was applied to the image classification of Olivetti Faces dataset.The experimental results show that the recognition accuracy is improved by 10%and the recognition speed is improved by about 37%when the image feature data processed by the hybrid algorithm are classified and recognized by convolutional neural network(CNN)algorithm.

关 键 词：人脸识别 特征提取 小波变换 主成分分析(PCA) 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]