Emotion recognition in the times of COVID19: Coping with face masks

•Emotion recognition with face mask•Iterative learning phases to approach the difficult tasks•Combination of state of the art Convolutional Neural Network Emotion recognition through machine learning techniques is a widely investigated research field, however the recent obligation to wear a face mask, following the COVID19 health emergency, precludes the application of systems developed so far. Humans naturally communicate their emotions through the mouth; therefore, the intelligent systems developed to date for identifying emotions of a subject primarily rely on this area in addition to other anatomical features (eyes, forehead, etc..). However, if the subject is wearing a face mask this region is no longer visible. For this reason, the goal of this work is to develop a tool able to compensate for this shortfall. The proposed tool uses the AffectNet dataset which is composed of eight class of emotions. The iterative training strategy relies on well-known convolutional neural network architectures to identify five sub-classes of emotions: following a pre-processing phase the architecture is trained to perform the task on the eight-class dataset, which is then recategorized into five classes allowing to obtain 96.92% of accuracy on the testing set. This strategy is compared to the most frequently used learning strategies and finally integrated within a real time application that allows to detect faces within a frame, determine if the subjects are wearing a face mask and recognize for each one the current emotion.