This paper presents our methodology for face and facial features detection to improve 3D face recognition in a presence of facial expression variation. Our goal was to develop an automatic process to be embedded in a face recognition system, using only range images as input. To do that, our approach combines traditional image segmentation techniques for face segmentation and detect facial features by combining an adapted method for 2D facial features extraction with the surface curvature information. The experiments were performed in a large, well-known face image database available on the Biometric Experimentation Environment (BEE), including 4,950 images. The results confirms that our method is efficient for the proposed application.