Improving 2D face recognition via fine-level facial depth generation and RGB-D complementary feature learning
This work addresses face recognition challenges like pose deformation and occlusion for security or biometric applications, representing an incremental improvement over existing RGB-D methods.
The paper tackles the problem of face recognition in complex scenes by proposing a fine-grained facial depth generation network and an improved multimodal complementary feature learning network, achieving state-of-the-art performance on the Lock3DFace and IIIT-D datasets.
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.