Face recognition based on eigen-illumination scheme and uncorrelated discriminant analysis

The illumination changes on face images make face recognition a very difficult task. In this paper, a human face representation scheme that is insensitive to illumination variation is proposed in order to deal with the problem. The variations in lighting over human faces are modeled by means of Principal Component Analysis (PCA) on a number of blurred faces under different lighting conditions. Then the 'difference image', which is the difference between the original image and the reconstructed image, is used for face recognition. We also propose an uncorrelated Linear Discriminant Analysis technique for face recognition based on the eigen-illumination representation scheme. This method can obtain the uncorrelated optimal discriminant vectors (UODVs) so that the extracted features are uncorrelated. Experimental results show that the proposed method is effective to deal with varying illumination problem for face recognition.