Automatic 2.5D face landmarking aims at locating facial feature points on 2.5D face models, such as eye corners, nose tip, etc. and has many applications ranging from face registration to facial expression recognition. In this paper, we propose a rotation invariant 2.5D face landmarking solution based on facial curvature analysis combined with a generic 2.5D face model and make use of a coarse-to-fine strategy for more accurate facial feature points localization. Experimented on more than 1600 face models randomly selected from the FRGC dataset, our technique displays, compared to a ground truth from a manual 3D face landmarking, a 100% of good nose tip localization in 8 mm precision and 100% of good localization for the eye inner corner in 12 mm precision.