计算机工程与应用2012，Vol.48Issue(27)：186-188,205,4.DOI:10.3778/j.issn.1002-8331.2012.27.039
结合DCT与KPCA的人脸识别
Face recognition based on DCT and KPCA
摘要
Abstract
As the nonlinear extensions of Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA) is effective for face recognition. In order to reduce recognition time, a face recognition method based on Discrete Cosine Transform (DCT) and KPCA is presented. The feature coefficients are extracted by DCT, and part of the coefficients are chosen to reconstruct face images. The face feature of high dimention is extracted by KPCA. The nearest neighbor classifier is used for identification. The experiment result on ORL face databases shows this method has the property of being faster, and the comprehensive performance is better than that of KPCA. Face recognition; feature extract; Kernel Principle Component Analysis (KPCA); Discrete Cosine关键词
人脸识别/特征提取/核主成分分析/离散余弦变换/最近邻分类器Key words
Transform (DCT)/ nearest neighbor classifier分类
信息技术与安全科学引用本文复制引用
刘嵩..结合DCT与KPCA的人脸识别[J].计算机工程与应用,2012,48(27):186-188,205,4.基金项目
湖北省自然科学基金(No.2009CDB069). （No.2009CDB069）
