  In this paper, we study performance and fairness on visual and thermal images
and expand the assessment to masked synthetic images. Using the SpeakingFace
and Thermal-Mask dataset, we propose a process to assess fairness on real
images and show how the same process can be applied to synthetic images. The
resulting process shows a demographic parity difference of 1.59 for random
guessing and increases to 5.0 when the recognition performance increases to a
precision and recall rate of 99.99\%. We indicate that inherently biased
datasets can deeply impact the fairness of any biometric system. A primary
cause of a biased dataset is the class imbalance due to the data collection
process. To address imbalanced datasets, the classes with fewer samples can be
augmented with synthetic images to generate a more balanced dataset resulting
in less bias when training a machine learning system. For biometric-enabled
systems, fairness is of critical importance, while the related concept of
Equity, Diversity, and Inclusion (EDI) is well suited for the generalization of
fairness in biometrics, in this paper, we focus on the 3 most common
demographic groups age, gender, and ethnicity.
