  Face recognition in complex scenes suffers severe challenges coming from
perturbations such as pose deformation, ill illumination, partial occlusion.
Some methods utilize depth estimation to obtain depth corresponding to RGB to
improve the accuracy of face recognition. However, the depth generated by them
suffer from image blur, which introduces noise in subsequent RGB-D face
recognition tasks. In addition, existing RGB-D face recognition methods are
unable to fully extract complementary features. In this paper, we propose a
fine-grained facial depth generation network and an improved multimodal
complementary feature learning network. Extensive experiments on the Lock3DFace
dataset and the IIIT-D dataset show that the proposed FFDGNet and I MCFLNet can
improve the accuracy of RGB-D face recognition while achieving the
state-of-the-art performance.
