Abstract
Face recognition has been a long-standing research field and especially boosted by the development of convolutional neural network (CNN). However, existing CNN based FR methods are known to exhibit bias - subjects in a certain demographic group can be better recognized than other groups. While previous works mostly focus on the fairness among different races, we argue that it is essential and favorable to measure the national as well as individual fairness of existing FR methods. As to achieve this, we contribute a dataset called National Faces in the World (NFW) to measure the fairness of representative state-of-the-art FR methods across different countries and individuals. We make comprehensive comparisons on our proposed NFW dataset and illustrate different degrees of bias of existing FR methods. We hope to facilitate more fair FR research with the proposed NFW. The dataset will be public for the whole research community at https://github.com/God-BlessYou/NFW.
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Sun, Y., Li, Y., Cui, Z. (2022). NFW: Towards National and Individual Fairness in Face Recognition. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13188. Springer, Cham. https://doi.org/10.1007/978-3-031-02375-0_40
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