Towards Fair Face Verification: An In-depth Analysis of Demographic
  Biases
Ioannis Sarridis Christos Koutlis Symeon Papadopoulos Christos Diou
Deep learning-based person identification and verification systems have
remarkably improved in terms of accuracy in recent years; however, such
systems, including widely popular cloud-based solutions, have been found to
exhibit significant biases related to race, age, and gender, a problem that
requires in-depth exploration and solutions. This paper presents an in-depth
analysis, with a particular emphasis on the intersectionality of these
demographic factors. Intersectional bias refers to the performance
discrepancies w.r.t. the different combinations of race, age, and gender
groups, an area relatively unexplored in current literature. Furthermore, the
reliance of most state-of-the-art approaches on accuracy as the principal
evaluation metric often masks significant demographic disparities in
performance. To counter this crucial limitation, we incorporate five additional
metrics in our quantitative analysis, including disparate impact and
mistreatment metrics, which are typically ignored by the relevant
fairness-aware approaches. Results on the Racial Faces in-the-Wild (RFW)
benchmark indicate pervasive biases in face recognition systems, extending
beyond race, with different demographic factors yielding significantly
disparate outcomes. In particular, Africans demonstrate an 11.25% lower True
Positive Rate (TPR) compared to Caucasians, while only a 3.51% accuracy drop is
observed. Even more concerning, the intersections of multiple protected groups,
such as African females over 60 years old, demonstrate a +39.89% disparate
mistreatment rate compared to the highest Caucasians rate. By shedding light on
these biases and their implications, this paper aims to stimulate further
research towards developing fairer, more equitable face recognition and
verification systems.