Automatic RGB Inference Based on Facial Emotion Recognition

Recently, Facial Emotion Recognition (FER) has been one of the most promising and growing field in computer vision and human-robot interaction. In this work, a deep learning neural network is introduced to address the problem of facial emotion recognition. In particular, a CNN+RNN architecture has been designed to capture both spatial features and temporal dynamics of facial expressions. Experiments are performed on CK+ dataset. Furthermore, we present a possible application of the proposed Facial Emotion Recognition system in human-robot interaction. A method for dynamically changing ambient light or LED colors, based on recognized emotions is presented. Indeed, it is proven that equipping robots with the ability of perceiving emotions and accordingly reacting by introducing suitable emphatic strategies significantly improves human-robot interaction performances. Possible scenarios of application are education, healthcare and autism therapy where such kind of emphatic strategies play a fundamental role.