Use of matrix polar decomposition for illumination-tolerant face recognition in discrete cosine transform domain

In this study, an illumination-tolerant face recognition algorithm is proposed. This work highlights the significance of matrix polar decomposition for illumination-invariant face recognition. The proposed algorithm has two stages. In the first stage, the authors reduce the effect of illumination changes by weakening the discrete cosine transform coefficients of block intensities using a new designed quantisation table. In the second stage, the unitary factor of polar decomposition of the reconstructed image is used as a feature matrix. In the recognition phase, a novel indirect method for measuring the similarities in feature matrices is proposed. The nearest-neighbour rule is applied to the matching. The authors have performed some experiments on several databases to evaluate the proposed method in its different aspects. Experimental results on recognition demonstrate that this approach provides a suitable representation for illumination invariant face recognition.