Recent research has shown that a better recognition performance can be attained through representing image sets as points on Grassmannian manifolds. However

the conventional discriminant analysis methods based on such manifolds take into account only the statistical information of labeled samples and suffer from ignoring unlabeled samples. To address this issue

a new method based on manifold regularization

called semi-supervised discriminant Analysis on Grassmannian Manifold(SDAGM)

is presented and applied to the image sets recognition problem. In SDAGM

a nearest neighbor graph is constructed to capture the local geometrical structure of all samples on the Grassmannian manifold and incorporates them into the objective function of discriminant analysis on Grassmannian manifold as a regularization term. Not only does the proposed algorithm consider the label information

but it also uses a consistency assumption. The feasibility and effectiveness of SDAGM are verified on several standard data sets with promising results.