Fair face recognition is all about learning invariant feature that
generalizes to unseen faces in any demographic group. Unfortunately, face
datasets inevitably capture the imbalanced demographic attributes that are
ubiquitous in real-world observations, and the model learns biased feature that
generalizes poorly in the minority group. We point out that the bias arises due
to the confounding demographic attributes, which mislead the model to capture
the spurious demographic-specific feature. The confounding effect can only be
removed by causal intervention, which requires the confounder annotations.
However, such annotations can be prohibitively expensive due to the diversity
of the demographic attributes. To tackle this, we propose to generate diverse
data partitions iteratively in an unsupervised fashion. Each data partition
acts as a self-annotated confounder, enabling our Invariant Feature
Regularization (INV-REG) to deconfound. INV-REG is orthogonal to existing
methods, and combining INV-REG with two strong baselines (Arcface and CIFP)
leads to new state-of-the-art that improves face recognition on a variety of
demographic groups. Code is available at
this https URL

公平人脸识别通过学习具有一般化效果的不变特征来应对真实世界观察中无偏常见的人口属性，其中存在偏差效应导致模型在少数群体中泛化效果不佳。我们指出这种偏差是由于潜在的人口属性导致不准确的特征学习，而这种混淆效应只能通过因果干预和消除潜在的人口属性才能解决。为了解决这个问题，我们提出了一种无监督生成多样性数据的分区方法，并结合两种强基准方法（Arcface 和 CIFP）与不变特征正则化（INV-REG）相结合，取得了新的最先进的结果，在各种人口群体中改善了人脸识别性能。