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.

翻译：复杂场景下的人脸识别面临姿态形变、光照不足、局部遮挡等扰动带来的严峻挑战。部分方法利用深度估计获取与RGB对应的深度信息以提升人脸识别精度，但其生成的深度图像存在模糊问题，为后续RGB-D人脸识别任务引入噪声。此外，现有RGB-D人脸识别方法难以充分提取互补特征。本文提出一种细粒度面部深度生成网络与改进的多模态互补特征学习网络。在Lock3DFace数据集和IIIT-D数据集上的大量实验表明，所提出的FFDGNet与IMCFLNet在实现最优性能的同时，可有效提升RGB-D人脸识别的准确率。