To facilitate the research on fairness of face recognition, one can directly download the models and feature vectors of RFW datasets.
In order to download the source images for further study, one must apply for ethnicity aware databases.
We also have uploaded the evaluation code on RFW, you can directly download them from here and use them to obtain the accuracy on 6000 pairs and the ROC curve on all pairs.• Feature_extract.zip. The code for extracting feature of your mxnet models. • Accuracy_for_RFW.zip. The evaluation code to get accuracy under LFW-like protocol. LFW-like protocol facilitates easy and fast comparison between algorithms with 6K pairs of images. • ROC_for_RFW.zip. The evaluation code to get ROC curve on all pairs of 3K identities (about 14K positive vs. 50M negative pairs).
Please cite the following if you make use of the dataset.
 Mei Wang, Weihong Deng, Jiani Hu, Xunqiang Tao, Yaohai Huang. Racial Faces in the Wild: Reducing Racial Bias by Information Maximization Adaptation Network. ICCV2019.
 Mei Wang, Weihong Deng. Mitigating Bias in Face Recognition using Skewness-Aware Reinforcement Learning. CVPR2020.
 Mei Wang, Weihong Deng. Deep face recognition: A Survey. arXiv:1804.06655.