  In image set classification, a considerable advance has been made by modeling
the original image sets by second order statistics or linear subspace, which
typically lie on the Riemannian manifold. Specifically, they are Symmetric
Positive Definite (SPD) manifold and Grassmann manifold respectively, and some
algorithms have been developed on them for classification tasks. Motivated by
the inability of existing methods to extract discriminatory features for data
on Riemannian manifolds, we propose a novel algorithm which combines multiple
manifolds as the features of the original image sets. In order to fuse these
manifolds, the well-studied Riemannian kernels have been utilized to map the
original Riemannian spaces into high dimensional Hilbert spaces. A metric
Learning method has been devised to embed these kernel spaces into a lower
dimensional common subspace for classification. The state-of-the-art results
achieved on three datasets corresponding to two different classification tasks,
namely face recognition and object categorization, demonstrate the
effectiveness of the proposed method.
