Synthetic Data for the Mitigation of Demographic Biases in Face
  Recognition
Pietro Melzi Christian Rathgeb Ruben Tolosana Ruben Vera-Rodriguez Aythami Morales Dominik Lawatsch Florian Domin Maxim Schaubert
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 potential to impact individuals from specific
demographic groups, and can be identified by observing disparate performance of
face recognition systems across demographic groups. They primarily arise from
the unequal representations of demographic groups in the training data. In
recent times, synthetic data have emerged as a solution to some problems that
affect face recognition systems. In particular, during the generation process
it is possible to specify the desired demographic and facial attributes of
images, in order to control the demographic distribution of the synthesized
dataset, and fairly represent the different demographic groups. We propose to
fine-tune with synthetic data existing face recognition systems that present
some demographic biases. We use synthetic datasets generated with GANDiffFace,
a novel framework able to synthesize datasets for face recognition with
controllable demographic distribution and realistic intra-class variations. We
consider multiple datasets representing different demographic groups for
training and evaluation. Also, we fine-tune different face recognition systems,
and evaluate their demographic fairness with different metrics. Our results
support the proposed approach and the use of synthetic data to mitigate
demographic biases in face recognition.