期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2012
卷号：9
期号：5
页码：221
DOI：10.5772/53786
语种：English
出版社：SAGE Publications
摘要：This paper presents a novel supervised dimensionality reduction approach for facial feature extraction called (2D)2LDALPP. The proposed (2D)2LDALPP method effectively combines alternative 2DLDA with alternative 2DLPP. The feature extraction is split into two steps: firstly, the column directional information is extracted by applying alternative 2DLDA; secondly, the feature matrix is inversed and alternative 2DLPP is used to extract the row directional information. The advantage of the method lies in the compression of the facial image in two different directions and the fact that the dimension of the feature matrix is low. At the same time, because 2DLDA is a supervised learning method, the proposed method not only preserves the manifold structure of the samples but also contains the label information of the classes. Experimental results on the Feret, ORL, and Yale databases show that the proposed method is effective.
关键词：Face Recognition; Linear Discriminant Analysis; Locality Preserving Projection
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