High-Level Context Representation for Emotion Recognition in Images

High-Level Context Representation for Emotion Recognition in Images

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 infl...

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Hauptverfasser: Costa, Willams de Lima, Martinez, Estefania Talavera, Figueiredo, Lucas Silva, Teichrieb, Veronica
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Computer Science - Computer Vision and Pattern Recognition
Computer Science - Human-Computer Interaction
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Martinez, Estefania Talavera
Figueiredo, Lucas Silva
Teichrieb, Veronica
description 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.
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Computer Science - Human-Computer Interaction
title High-Level Context Representation for Emotion Recognition in Images
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