Although deep face recognition has achieved impressive progress in recent years,
controversy has arisen regarding discrimination based on skin tone, questioning their …

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 …

Recently, large-margin softmax loss methods, such as angular softmax loss (SphereFace),
large margin cosine loss (CosFace), and additive angular margin loss (ArcFace), have …

Racial equality is an important theme of international human rights law, but it has been
largely obscured when the overall face recognition accuracy is pursued blindly. More facts …

Face recognition systems are widely deployed in safety-critical applications, including law
enforcement, yet they exhibit bias across a range of socio-demographic dimensions, such as …

We propose a discrimination-aware learning method to improve both the accuracy and
fairness of biased face recognition algorithms. The most popular face recognition …

State-of-the-art deep face recognition methods are mostly trained with a softmax-based multi-
class classification framework. Despite being popular and effective, these methods still have …

Current face recognition systems achieve high progress on several benchmark tests.
Despite this progress, recent works showed that these systems are strongly biased against …

In spite of the high performance and reliability of deep learning algorithms in a wide range of
everyday applications, many investigations tend to show that a lot of models exhibit biases …

As a widely used loss function in deep face recognition, the softmax loss cannot guarantee
that the minimum positive sample-to-class similarity is larger than the maximum negative …