Biometrics is an emerging area of bioengineering that pursues the characterization of a person by means of something that the person is or produces. Face recognition is a particularly attractive biometric challenge. Most of the face recognition research performed in the past used 2D intensity images. However, algorithms based on 2D images are not robust to changes of illumination in the environment or orientation of the subject. The ability to acquire 3D scans of human faces removes those ambiguities, since they capture the exact geometry of the subject, invariant to illumination and orientation changes. Unencumbered by those limitations, research in 3D face recognition is now beginning to address a different source of error in biometric recognition: facial geometry deformation caused by facial expressions, which can make 3D algorithms which treat 3D faces as rigid surfaces fail. In this paper, a 3D face recognition framework is proposed to tackle this problem. The framework is composed of three subsystems: expression recognition system, expressional face recognition system and neutral face recognition system. In particular, a system for the recognition of faces with one type of expression (smile) and neutral faces was implemented and tested on a database of 30 subjects. The results proved the feasibility of this framework.