  Emotion recognition is the task of classifying perceived emotions in people.
Previous works have utilized various nonverbal cues to extract features from
images and correlate them to emotions. Of these cues, situational context is
particularly crucial in emotion perception since it can directly influence the
emotion of a person. In this paper, we propose an approach for high-level
context representation extraction from images. The model relies on a single cue
and a single encoding stream to correlate this representation with emotions.
Our model competes with the state-of-the-art, achieving an mAP of 0.3002 on the
EMOTIC dataset while also being capable of execution on consumer-grade hardware
at approximately 90 frames per second. Overall, our approach is more efficient
than previous models and can be easily deployed to address real-world problems
related to emotion recognition.
