自动化学报2005，Vol.31Issue(5)：782-787,6.
关于二维主成分分析方法的研究
Is Two-dimensional PCA a New Technique?
摘要
Abstract
The principal component analysis (PCA), or the eigenfaces method, is a de facto standard in human face recognition. Numerous algorithms tried to generalize PCA in different aspects. More recently, a technique called two-dimensional PCA (2DPCA) was proposed to cut the computational cost of the standard PCA. Unlike PCA that treats images as vectors, 2DPCA views an image as a matrix. With a properly defined criterion, 2DPCA results in an eigenvalue problem which has a much lower dimensionality than that of PCA. In this paper, we show that 2DPCA is equivalent to a special case of an existing feature extraction method, i.e., the block-based PCA. Using the FERET database, extensive experimental results demonstrate that block-based PCA outperforms PCA on datasets that consist of relatively simple images for recognition, while PCA is more robust than 2DPCA in harder situations.关键词
Face recognition/PCA/two-dimensional PCA/block-based PCAKey words
Face recognition/PCA/two-dimensional PCA/block-based PCA分类
信息技术与安全科学引用本文复制引用
王立威,王潇,常明,封举富..关于二维主成分分析方法的研究[J].自动化学报,2005,31(5):782-787,6.基金项目
Supported by National Key Basic Research Project of R. P. China (2004CB318000) （2004CB318000）
