Matching faces from images of different specters is still nowadays a challenging task. In this seminar, we present a survey on how well preprocessing techniques perform in Near Infrared (NIR) and visible-light (VIS) face matching. The following preprocessing techniques are going to be de-scripted and evaluated using the pre-trained model VGG-Face. Local Binary Patterns (LBP), Difference of Gaussian(DoG), Laplacian of Gaussian (LoG), Histogram Equalization (HE), Adaptive Single Scale Retinex (ASSR), Ho-momorphic Filtering (HOMO), Isotropic smoothing (IS), Single Scale Self Quotient Image (SSQ), Single Scale Weber Faces Normalization (WEB). Experimentation was per- formed using CASIA NIR-VIS 2.0 Face Database. Results indicate that Histogram Equalization was the best preprocessing technique of the nine in various aspects.