Regularized constraint subspace based method for image set classification

Subspace methods are popular for image set classification due to the excellent representation ability of subspaces. Generalized difference subspace and orthogonal subspace are two currently effective projection strategies for extracting discriminative subspaces. However, both of these methods discard part of the common subspace to form the constraint subspace, which may cause a loss of discriminative information. In this work, we combine the difference subspace and orthogonal subspace to form a full rank constraint subspace. Moreover, we generalize this approach to a common framework using eigenspectrum regularization models (ERMs). The full rank constraint subspace that is regularized by different ERMs is called the regularized constraint subspace (RCS). Furthermore, we propose a new ERM using the concept of difference subspace, namely, the difference subspace regularization model (DSRM). The DSRM and two other current ERMs are incorporated in our RCS-based framework. The results from extensive experiments have demonstrated the effectiveness of our proposed approaches.