Face recognition has a wide range of applications in daily life, and it is of great practical significance to improve the accuracy of face recognition. The current 2D face recognition technology has an accuracy rate close to 99% under ideal conditions, but its performance is still affected by factors like lighting changes and occlusion. The rapid development of 3D face reconstruction technology has greatly enhanced the ability to create 3D faces from 2D images. By parameterizing the face model for 3D reconstruction, an occlusion-free 3D face can be generated, thus effectively addressing the challenges in 2D recognition. To address the above problems, this paper proposes a 3D face recognition method based on the DECA model. By combining the 3D face shape maps produced by the DECA model with the traditional 2D color maps, the accuracy of feature extraction is improved. We evaluated our method through closed-set testing using the Texas3DFRD dataset. The experimental results show that using both 2D color maps and 3D reconstruction-generated shape maps as input for feature extraction can significantly improve the accuracy of face matching and recognition, verifying the advantages of 3D face reconstruction technique in recognition tasks.