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This paper proposes a two-step subspace learning framework by combining non-linear kernel PCA (KPCA) and with contextual constraints based linear discriminant analysis (CCLDA) for face recognition. The linear CCLDA approach does not consider the higher order non-linear information in facial images, whereas the wide face variations posed by some factors, such as viewpoint, illumination and expression,...
In this paper, two new methods: ECA and 2DECA are proposed for face recognition, which are inspired by KECA. In ECA (2DECA), features are selected in PCA (2DPCA) subspace based on the Renyi entropy contribution instead of cumulative variance contribution. Then the proposed methods are tested on the OLR, YALE and XM2VTS databases respectively. We also compare the performance of the related methods...
In this paper, we first propose a method to transform from LDA to PCA with the discriminative information embedded in a whitening transformation, and then we propose a simple support vector machine formulation to LDA. The results of experiments of face recognition conducted on ORL database show the effectiveness of the proposed method.
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