MixFairFace: Towards Ultimate Fairness via MixFair Adapter in Face Recognition

TL;DR

MixFairFace introduces a novel framework that enhances fairness in face recognition by addressing identity bias without relying on sensitive attribute labels, achieving state-of-the-art results.

Contribution

The paper proposes MixFair Adapter and a new fairness evaluation protocol that focus on identity bias, improving fairness without sensitive attribute labels.

Findings

Achieves state-of-the-art fairness on benchmark datasets.

Reduces identity bias effectively in face recognition models.

Proposes a new fairness evaluation protocol for face recognition.

Abstract

Although significant progress has been made in face recognition, demographic bias still exists in face recognition systems. For instance, it usually happens that the face recognition performance for a certain demographic group is lower than the others. In this paper, we propose MixFairFace framework to improve the fairness in face recognition models. First of all, we argue that the commonly used attribute-based fairness metric is not appropriate for face recognition. A face recognition system can only be considered fair while every person has a close performance. Hence, we propose a new evaluation protocol to fairly evaluate the fairness performance of different approaches. Different from previous approaches that require sensitive attribute labels such as race and gender for reducing the demographic bias, we aim at addressing the identity bias in face representation, i.e., the performance inconsistency between different identities, without the need for sensitive attribute labels. To this end, we propose MixFair Adapter to determine and reduce the identity bias of training samples. Our extensive experiments demonstrate that our MixFairFace approach achieves state-of-the-art fairness performance on all benchmark datasets.