Taking the Hidden Route: Deep Mapping of Affect via 3D Neural Networks

In this note we address the problem of providing a fast, automatic, and coarse processing of the early mapping from emotional facial expression stimuli to the basic continuous dimensions of the core affect representation of emotions, namely valence and arousal. Taking stock of results in affective neuroscience, such mapping is assumed to be the earliest stage of a complex unfolding of processes that eventually entail detailed perception and emotional reaction involving the proper body. Thus, differently from the vast majority of approaches in the field of affective facial expression processing, we assume and design such a feedforward mechanism as a preliminary step to provide a suitable prior to the subsequent core affect dynamics, in which recognition is actually grounded. To this end we conceive and exploit a 3D spatiotemporal deep network as a suitable architecture to instantiate such early component, and experiments on the MAHNOB dataset prove the rationality of this approach.