Contextual constraints based kernel discriminant analysis for face recognition
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, existing in nonlinear subspaces may lead to many difficulties in face recognition and classification problems. To counteract the above problem, we incorporate the contextual information into kernel discriminant analysis by using KPCA in a two-step process, which provides more useful information for face recognition and classification. Experimental results on three well-known face databases, ORL, Yale and XM2VTS, validate the effectiveness of the proposed method. (4 pages)