  We propose two face representations that are blind to facial expressions
associated to emotional responses. This work is in part motivated by new
international regulations for personal data protection, which enforce data
controllers to protect any kind of sensitive information involved in automatic
processes. The advances in Affective Computing have contributed to improve
human-machine interfaces but, at the same time, the capacity to monitorize
emotional responses triggers potential risks for humans, both in terms of
fairness and privacy. We propose two different methods to learn these
expression-blinded facial features. We show that it is possible to eliminate
information related to emotion recognition tasks, while the performance of
subject verification, gender recognition, and ethnicity classification are just
slightly affected. We also present an application to train fairer classifiers
in a case study of attractiveness classification with respect to a protected
facial expression attribute. The results demonstrate that it is possible to
reduce emotional information in the face representation while retaining
competitive performance in other face-based artificial intelligence tasks.
