A new preprocessing algorithm for illumination invariant face recognition called Segmented Local Normalization (SLN) is proposed. The main idea is to produce image segmentation so that in each segment

pixel points have similar surface normal distribution and then have similar intensity responses to the light source. Then the local pixel normalization is processed in each segment in order to eliminate illumination. The algorithm firstly establishes Lambert object surface reflection model and secondly a general face surface normal matrix is estimated using SVD. Then the clustering algorithm based on the surface normal directions is used to obtain the image segments

and a local normalization is applied in each image segment. Finally

the traditional face recognition algorithm like PCA is applied on the normalized images. Experimental results based on the Harvard and YaleB face database show that under uneven illumination conditions

the algorithm can increase the face recognition rate efficiently.