A fusion framework is introduced in this chapter to demonstrate the feasibility of integrating 2D and 3D face recognition systems. Specifically, four convolution filters based on wavelet functions (Gaussian derivative, Morlet, complex Morlet, and complex frequency B-spline) are applied to extract the convolution features from the 2D and 3D image modalities to capture the intensity texture and curvature shape, respectively. The convolution features are then used to compute two separate similarity measures for the 2D and 3D modalities, which are later linearly fused to calculate the final similarity measure. The feasibility of the proposed method is demonstrated using the Face Recognition Grand Challenge (FRGC) version 2 Experiment 3, which contains 4,950 2D color images (943 controlled and 4,007 uncontrolled) and 4,950 3D recordings. The experimental results show that the Gaussian derivative convolution filter extracts the most discriminating features from the 3D modality among the four filters, and the complex frequency B-spline convolution filter outperforms the other filters when the 2D modality is applied.