Perceived emotion from images through deep neural networks
Álex Hernández-García · 2017
One of the goals of affective computing is predicting the emotional response of people elicited by multimedia content. Although remarkable steps have been made in the field, the problem still remains open. Emotions are conveyed by many and varied factors, from very low-level cues, such as the colors of the stimulus, to high-level aspects, such as the semantics of the scene. One of the main challenges is the fact that even though some of these factors are known by neuro-scientists, computer scientists or artists, many of the stimulus features that play a role in eliciting emotion probably remain unknown. The recent success of deep learning methods, which are able to automatically learn relevant stimuli representations, seems to set a promising path to follow. Here we will explore new deep neural architectures suitable for affective content analysis, together with semi-supervised models that help handle the relative lack of available data and the uncertainty and subjectivity of the annotations.