Though 2D face recognition (FR) has achieved great success due to powerful 2D CNNs and large-scale training data, it is still challenged by extreme poses and illumination conditions. On the other hand, 3D FR has the potential to deal with aforementioned challenges in the 2D domain. However, most of available 3D FR works transform 3D surfaces to 2D maps and utilize 2D CNNs to extract features. The works directly processing point clouds for 3D FR is very limited in literature. To bridge this gap, in this paper, we propose a light-weight framework, named PointFace, to directly process point set data for 3D FR. Inspired by contrastive learning, our PointFace use two weight-shared encoders to directly extract features from a pair of 3D faces. A feature similarity loss is designed to guide the encoders to obtain discriminative face representations. We also present a pair selection strategy to generate positive and negative pairs to boost training. Extensive experiments on Lock3DFace and Bosphorus show that the proposed PointFace outperforms state-of-the-art 2D CNN based methods.

3D face recognition (FR) is a popular topic in computer vision, since 3D face data is invariant to pose and illumination condition changes which easily affect the performance of 2D FR. Though many 3D solutions have achieved impressive performances on public high-quality 3D face databases, few works concentrate on low-quality 3D FR. As the quality of 3D face acquired by widely used low-cost RGB-D sensors is really low, more robust methods are required to achieve satisfying performance on these 3D face data. To address this issue, we propose a novel two-stage pipeline to improve the performance of 3D FR. In the first stage, we utilize pix2pix network to restore the quality of low-quality face. In the second stage, we launch a multi-quality fusion network (MQFNet) to fuse the features from different qualities and enhance FR performance. Our proposed network achieves the state-of-the-art performance on the Lock3DFace database. Furthermore, extensive controlled experiments are conducted to demonstrate the effectiveness of each model of our network.

Recent studies show that there exist significant racial bias among state-of-the-art (SOTA) face recognition algorithms, i.e., the accuracy for Caucasian is consistently higher than that for other races like African and Asian. To mitigate racial bias, we propose the race adaptive margin based face recognition (RamFace) model, designed under the multi-task learning framework with the race classification as the auxiliary task. The experiments show that the race classification task can enforce the model to learn the racial features and thus improve the discriminability of the extracted feature representations. In addition, a racial bias robust loss function, i.e., race adaptive margin loss, is proposed such that different optimal margins can be automatically derived for different races in training the model, which further mitigates the racial bias. The experimental results show that on RFW dataset, our model not only achieves SOTA face recognition accuracy but also mitigates the racial bias problem. Besides, RamFace is also tested on several public face recognition evaluation benchmarks, i.e., LFW, CPLFW and CALFW, and achieves better performance than the commonly used face recognition methods, which justifies the generalization capability of RamFace.