This article presents FRCSyn-onGoing, an ongoing challenge for face recognition where
researchers can easily benchmark their systems against the state of the art in an open …

This study investigates the possibility of mitigating the demographic biases that affect face
recognition technologies through the use of synthetic data. Demographic biases have the …

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 …

Making models algorithmically fairer in tabular data has been long studied, with techniques
typically oriented towards fixes which usually take a neural model with an undesirable …

Deep neural networks have achieved impressive performance on many computer vision
benchmarks in recent years. However, can we be confident that impressive performance on …

Demographic bias in deep learning-based face recognition systems has led to serious
concerns. Several existing works attempt to mitigate bias by incorporating demographic …

The proliferation of automated face recognition in the commercial and government sectors
has caused significant privacy concerns for individuals. One approach to address these …

Algorithmic systems are increasingly being applied in contexts of state action to, in some
capacity, mediate the relations between state and individual. Disadvantageous effects, such …

Demographic bias in deep learning-based face recognition systems has led to serious
concerns. Often, the biased nature of models is attributed to severely imbalanced datasets …

In this paper, we explore the trade-off between fairness and accuracy when data is biased
and unbiased. We introduce two versions of a modified loss function: Group Equity and …