High-level context representation for emotion recognition in images

Willams De Lima Costa*, Estefania Talavera Martínez, Lucas Silva Figueiredo, Veronica Teichrieb

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

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 ≈ 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.

Original languageEnglish
Title of host publication2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
PublisherIEEE
Pages326-334
Number of pages9
ISBN (Electronic)9798350302493
DOIs
Publication statusPublished - 2023
EventIEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023 - Vancouver, Canada
Duration: 18 Jun 202322 Jun 2023

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2023-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

ConferenceIEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
Abbreviated titleCVPRW 2023
Country/TerritoryCanada
CityVancouver
Period18/06/2322/06/23

Keywords

  • 2024 OA procedure

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