  In our everyday lives and social interactions we often try to perceive the
emotional states of people. There has been a lot of research in providing
machines with a similar capacity of recognizing emotions. From a computer
vision perspective, most of the previous efforts have been focusing in
analyzing the facial expressions and, in some cases, also the body pose. Some
of these methods work remarkably well in specific settings. However, their
performance is limited in natural, unconstrained environments. Psychological
studies show that the scene context, in addition to facial expression and body
pose, provides important information to our perception of people's emotions.
However, the processing of the context for automatic emotion recognition has
not been explored in depth, partly due to the lack of proper data. In this
paper we present EMOTIC, a dataset of images of people in a diverse set of
natural situations, annotated with their apparent emotion. The EMOTIC dataset
combines two different types of emotion representation: (1) a set of 26
discrete categories, and (2) the continuous dimensions Valence, Arousal, and
Dominance. We also present a detailed statistical and algorithmic analysis of
the dataset along with annotators' agreement analysis. Using the EMOTIC dataset
we train different CNN models for emotion recognition, combining the
information of the bounding box containing the person with the contextual
information extracted from the scene. Our results show how scene context
provides important information to automatically recognize emotional states and
motivate further research in this direction. Dataset and code is open-sourced
and available at: https://github.com/rkosti/emotic and link for the
peer-reviewed published article: https://ieeexplore.ieee.org/document/8713881
