二维局部主成分分析在人脸识别中的应用

2008 Chinese Conference on Pattern Recognition Pub Date : 2008-10-31 DOI:10.1109/CCPR.2008.52

Yu-sheng Lin, Jianguo Wang, Jing-yu Yang

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引用次数: 1

摘要

本文提出了一种直接基于图像矩阵而非一维图像向量的人脸识别特征提取方法——二维局部主成分分析(2DLPCA)。dlpca旨在发现图像的内在局部结构。这个局部结构可能包含有用的判别信息。在ORL人脸数据库上的实验结果表明了该算法的有效性。

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Two Dimension Locally Principal Component Analysis for Face Recognition

In this paper, we propose a feature extraction method called two dimension locally principal component analysis (2DLPCA) for face recognition, which is based directly image matrix rather than 1D image vectors. 2DLPCA seeks to discover the intrinsic image local structure. This local structure may contain useful information for discrimination. Experimental results on ORL face database show the effectiveness of the proposed algorithm.

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2008 Chinese Conference on Pattern Recognition

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