3D stereophotography is rapidly being adopted by medical researchers for analysis of facial forms and features. An essential step for many applications using 3D face data is to first crop the head and face from the raw images. The goal of this paper is to develop a reliable automatic methodology for extracting the face from raw data with texture acquired from a stereo imaging system, based on the medical researchers' specific requirements. We present an automated process, including eye and nose estimation, face detection, Procrustes analysis and final noise removal to crop out the faces and normalize them. The proposed method shows very reliable results on several datasets, including a normal adult dataset and a very challenging dataset consisting of infants with cleft lip and palate.