In the absence of lighting, due to the difference in the modality, it is very difficult to match the visible face database with the face images obtained in the thermal wavelength. Applying photometric preprocessing to reduce the modality difference, increases the face recognition performance from thermal to visible. In this study, different photometric preprocessing methods that can be used for face recognition problem between the images in the visible and thermal images were analyzed. Photometric methods have been tested in two different databases and Rank-k successes of the methods were compared. When Weberface normalization is used instead of DOG photometric preprocessing widely used in the literature, Rank-1 success has increased by 2-3%.