Face recognition is a difficult task. It has to deal with various challenges such as lighting, orientation, and variability among different faces. Previous work has shown that the 3D face is a robust biometric modality, less affected by variations in pose or light. In addition, with the availability of depth cameras, capturing 3D data has become much easier. We propose a multi-scale framework for 3D face recognition. First, 3D point clouds are preprocessed and presented at different scales; each scale is then converted to depth maps. Next, the statistical measurements are extracted based on fast grid-based models. After that, the histogram of the oriented gradient descriptor is applied to encode the statistical grids. Finally, the features are projected and postprocessed to increase the range of data distinctiveness. Our proposed technique shows encouraging performance on Bosphorus and GavabDB databases. The recognition accuracies of the proposed algorithm achieved an overall rank-1 identification rate of 98.91% on the GavabDB database and 99.89% on the Bosphorus database.