Modern face recognition (FR) systems are now based on Deep Convolutional
Neural Networks (DCNNs) and present skewed recognition scores towards
covariates of a test population (i.e. biased with respect to age, gender
and ethnicity). There is a pressing need for fair FR systems and
therefore to develop techniques to reduce biases in FR. The CITeR
project FairFace is focused on the investigation of regularization
mechanisms to mitigate biases by controlling the parameters of an
arbitrary DCNN depending on specific cohorts. The project will benefit
from public open datasets containing the covariates of interest.