Recognition through constructing the Eigenface classifiers using conjugation indices

The principal component analysis (PCA), also called the eigenfaces analysis, is one of the most extensively used face image recognition techniques. The idea of the method is decomposition of image vectors into a system of eigenvectors matched to the maximum eigenvalues. The method of proximity assessment of vectors composed of principal components essentially influences the recognition quality. In the paper the use of different indices of conjugation with subspace stretched on training vectors is considered as a proximity measure. It is shown that this approach is very effective in the case of a small number of training examples. The results of experiments for a standard ORL-face database are presented.