This report addresses the problem of locating facial features in images of frontal faces taken under different lighting conditions. The well-known Active Shape Model method proposed by Cootes {\it et al.} is extended in order to improve its robustness to illumination changes. For that purpose, we introduce the use of Local Binary Patterns (LBP). Three different approaches combining ASM with LBP are presented: profile-based LBP-ASM, square-based LBP-ASM and divided-square-based LBP-ASM. Experiments performed on the standard and darkened image sets of the XM2VTS database demonstrate that the divided-square-based LBP-ASM gives superior performance compared to the state-of-the-art ASM. It achieves more accurate results and fails less frequently.