一种用于人脸识别的非迭代GLRAM算法

doi:10.3969/j.issn.1001-2400.2014.02.024

Abstract

Abstract:

In this paper, we get the right projection transform matrix by the covariance matrix of the 2DPCA algorithm, and gain the left projection transform matrix by dimensional reduction of the feature matrix of the 2DPCA. Then we propose a new non-iterative algorithm for generalized low rank approximation of matrices (NGLRAM).Experiments on ORL and AR face database show that the new NGLRAM saves a lot of training time to get the similar performance with GLRAM in image reconstruction and image recognition. Compared with the 2DPCA, the NGLRAM can lead to better results in image reconstruction and image recognition at a larger compression rate.

Key words: face recognition, data dimension reduction, generalized low rank approximations of matrices(GLRAM), two-dimensional principal component analysis(2DPCA)

ZHAO Yangyang;ZHOU Shuisheng;WU Yajing. Non-iterative GLRAM algorithm for face recognition[J].J4, 2014, 41(2): 144-150.

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Non-iterative GLRAM algorithm for face recognition

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