SA-SVD: Mitigating Bias in Face Recognition by Fair Representation Learning

Jia Li, Hua Zhang

Abstract: Rapid advancements in face recognition systems have revolutionized the field, enabling the efficiency and accuracy of various applications. Despite the significant progress achieved, these systems still face various challenges, with one important issue revolving around the potential biases and unfairness they may exhibit. These systems are likely to exhibit homogeneity bias, which refers to their tendency to erroneously classify faces belonging to the same gender or ethnic group together. Empirical observations indicate that the current representations of face data do not fully exploit the inherent dimension and expressive capacity of the representation space, leading to a diversity gap in representations for various groups. Consequently, under-utilized representation space for minority groups leads to bias in face recognition. In this paper, to enhance the representation space utilization and reduce disparities among different demographic groups, we introduce SA-SVD, a regularization method orthogonal to existing face recognition methods. It has the potential to capture the intricate patterns and subtle differences in face features across individuals within certain demographic groups. Extensive experiments demonstrate our method’s effectiveness in mitigating biased outcomes and achieving superior performance on two benchmark datasets VGGFace2 and CelebA.