Today, face figures among the most promising biometrics, allowing to identify people without requiring any physical contact. In this research field, 3D provides a significant improvement in recognition performances, but the existing approaches show limitations dealing with pose variations; indeed 3D face surfaces need to be aligned before the matching operation. This paper proposes an approach that overcomes this limitation by projecting the 3D shape information onto the 2D surface of a normal sphere, while a rotation invariant descriptor is used to extract key features from this surface. In addition, using a 2D descriptor reduces the computing time that is a typical drawback of 3D methods. Experimentations have been conducted on a property face dataset, to assess the robustness of the method with respect to a large set of facial expression and pose variations.