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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 the client specific kernel discriminant analysis (CSKDA) is studied. The theory of CSKDA, which is the nonlinear model of the previously suggested model of client specific linear discriminant analysis, is proposed by using kernel technique. A new CSKDA subspace method is developed in order to reduce the computational complexity. Results of experiments conducted on the internationally...
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