  Contactless and efficient systems are implemented rapidly to advocate
preventive methods in the fight against the COVID-19 pandemic. Despite the
positive benefits of such systems, there is potential for exploitation by
invading user privacy. In this work, we analyse the privacy invasiveness of
face biometric systems by predicting privacy-sensitive soft-biometrics using
masked face images. We train and apply a CNN based on the ResNet-50
architecture with 20,003 synthetic masked images and measure the privacy
invasiveness. Despite the popular belief of the privacy benefits of wearing a
mask among people, we show that there is no significant difference to privacy
invasiveness when a mask is worn. In our experiments we were able to accurately
predict sex (94.7%),race (83.1%) and age (MAE 6.21 and RMSE 8.33) from masked
face images. Our proposed approach can serve as a baseline utility to evaluate
the privacy-invasiveness of artificial intelligence systems that make use of
privacy-sensitive information. We open-source all contributions for
re-producibility and broader use by the research community.
