Despite the major advances, the accuracy of modern face verification systems depends on the lighting conditions. The variability of illumination can be compensated either by performing image preprocessing or by training more robust verification models. Nowadays, great priority is given to the development of neural network classifiers, while the importance of image preprocessing is being undeservedly neglected. This article proposes a method for spatially weighted brightness normalization of grayscale face images which preserves the relevant image information. An experimental study is performed to demonstrate the effects of various methods for brightness normalization on the accuracy of the neural network classifier in the application of face verification. It is shown that brightness normalization can improve the face verification accuracy for images captured in complex illumination conditions, that is, to compensate for samples that were not fully present in the training data.