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Face recognition in complex scenes suffers severe challenges coming fromperturbations such as pose deformation, ill illumination, partial occlusion.Some methods utilize depth estimation to obtain depth corresponding to RGB toimprove the accuracy of face recognition. However, the depth generated by themsuffer from image blur, which introduces noise in subsequent RGB-D facerecognition tasks. In addition, existing RGB-D face recognition methods areunable to fully extract complementary features. In this paper, we propose afine-grained facial depth generation network and an improved multimodalcomplementary feature learning network. Extensive experiments on the Lock3DFacedataset and the IIIT-D dataset show that the proposed FFDGNet and I MCFLNet canimprove the accuracy of RGB-D face recognition while achieving thestate-of-the-art performance.

Improving 2D face recognition via fine-level facial depth generation and RGB-D complementary feature learning