Dream Net: a privacy preserving continual leaming model for face emotion recognition

Abstract

Continual learning is a growing challenge of artificial intelligence. Among algorithms alleviating catastrophic forgetting that have been developed in the past years, only few studies were focused on face emotion recognition. In parallel, the field of emotion recognition raised the ethical issue of privacy preserving. This paper presents Dream Net, a privacy preserving continual learning model for face emotion recognition. Using a pseudo-rehearsal approach, this model alleviates catastrophic forgetting by capturing the mapping function of a trained network without storing examples of the learned knowledge. We evaluated Dream Net on the Fer-2013 database and obtained an average accuracy of 45% ± 2 at the end of incremental learning of all classes compare to 16% ± 0 without any continual learning model.