This paper 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 to improve its robustness to illumination changes. For that purpose, we introduce the use of Local Binary Patterns (LBP). Three different incremental 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.