CG-MCFNet: cross-layer guidance-based multi-scale correlation fusion network for 3D face recognition

3D face recognition (FR) has been a popular field in recent years, which benefits from the advancement of 3D sensors and the application demands of video surveillance scenes. Existing 3D FR methods could show excellent performance when faces are complete. However, incomplete 3D faces, especially large poses and occluded, may prevent the model to learn effective and strong discriminative facial information adequately, resulting in unsatisfactory recognition results. To address this issue, we propose a cross-layer guidance-based multi-scale correlation fusion network (CG-MCFNet) for 3D FR. Firstly, we design a shallow feature enhancement extraction (SFE) module to obtain more effective facial detail information, and a deep feature enhancement extraction (DFE) module to learn more information with strong discrimination. Secondly, a novel multi-scale feature correlation fusion (MCF) module is proposed for fusing features from different layers, aiming to reduce the interference of redundant features and enhance the acquisition of discriminative features. Finally, the above three modules are integrated to form a new multi-scale local feature extraction (MLFE) module for capturing the face local information of rich and more strong discriminative. In addition, we introduce a global and local feature similarity weighted joint inference strategy, to further improve recognition accuracy. Extensive experiments on six challenging datasets, including three low-quality datasets (Lock3DFace, KinectFaceDB, and IIIT-D, where Lock3DFace is a video dataset), two high-quality datasets (UMB-DB, Bosphorus), and a cross-quality dataset synthesized by Bosphorus, prove that our CG-MCFNet achieves the best performance for incomplete 3D FR, which demonstrates the strong generalization ability of our model.