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基于小波变换和主分量分析的人脸识别
Face Recognition Based on Wavelet Transform and Principal Component Analysis
【摘要】 提出了基于小波变换和主分量分析的人脸识别算法.该算法首先用小波变换对人脸图像进行小波分解,形成低频小波子图,然后用主分量分析法构造特征脸子空间,将人脸图像在特征空间的投影作为KNN分类器的输入,由KNN分类器对提取的特征进行识别.在ORL人脸数据库上的实验结果表明该方法具有良好的性能.
【Abstract】 Wavelet transform combined with principal component analysis is applied to human face recognition. After extracting low frequency sub-band of face image in wavelet transform,the eigenface is constructed by PCA.Then all samples are projected into the subspace,the coefficient of every sample is inputted K-Nearest Neighbor,and the face recognizer consists of KNN.The experiments on ORL face database indicate that the recognition ratio is greatly improved.
【关键词】 人脸识别;
小波变换(WT);
主分量分析(PCA);
KNN分类器;
【Key words】 face recognition; wavelet transform(WT); principal component analysis(PCA); K-Nearest Neighbor(KNN);
【Key words】 face recognition; wavelet transform(WT); principal component analysis(PCA); K-Nearest Neighbor(KNN);
【基金】 张家口市科技局指导性计划项目(092128B)
- 【文献出处】 河北建筑工程学院学报 ,Journal of Hebei Institute of Architecture and Civil Engineering , 编辑部邮箱 ,2010年01期
- 【分类号】TP391.41
- 【被引频次】5
- 【下载频次】136