The Harms of Demographic Bias in Deep Face Recognition Research

In this work we demonstrate the existence of demographic bias in the face representations of currently popular deep-learning-based face recognition models, exposing a bad research and development practice that may lead to a systematic discrimination of certain demographic groups in critical scenarios like automated border control. Furthermore, through the simulation of the template morphing attack, we reveal significant security risks that derive from demographic bias in current deep face models. This widely ignored problem poses important questions on fairness and accountability in face recognition.