Two-dimensional subspace classifiers for face recognition

The subspace classifiers are pattern classification methods where linear subspaces are used to represent classes. In order to use the classical subspace classifiers for face recognition tasks, two-dimensional (2D) image matrices must be transformed into one-dimensional (1D) vectors. In this paper, we propose new methods to apply the conventional subspace classifier methods directly to the image matrices. The proposed methods yield easier evaluation of correlation and covariance matrices, which in turn speeds up the training and testing phases of the classification process. Utilizing 2D image matrices also enables us to apply 2D versions of some subspace classifiers to the face recognition tasks, in which the corresponding classical subspace classifiers cannot be used due to high dimensionality. Moreover, the proposed methods are also generalized such that they can be used with the higher order image tensors. We tested the proposed 2D methods on three different face databases. Experimental results show that the performances of the proposed 2D methods are typically better than the performances of classical subspace classifiers in terms of recognition accuracy and real-time efficiency.