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Combined subspace method using global and local features for face recognition

This paper proposes a combined subspace method using both global and local features for face recognition. The global and local features are obtained by applying the LDA-based method to either the whole or part of a face image, respectively. The combined space is constructed with the projection vectors corresponding to large eigenvalues of the between-class scatter matrix in each subspace. It is based on the fact that the eigenvectors corresponding to larger eigenvalues have more discriminating power. The combined subspace is evaluated in view of the Bayes error, which shows how well samples can be classified. The combined subspace gives small Bayes error than the subspaces composed of either the global or local features. Comparative experiments are also performed using the color FERET database of facial images. The experimental results show that the combined subspace method gives better recognition rate than other methods.