research-article

Emotion recognition with multimodal features and temporal models

Authors:

Yong QinAuthors Info & Claims

ICMI '17: Proceedings of the 19th ACM International Conference on Multimodal Interaction

Pages 598 - 602

https://doi.org/10.1145/3136755.3143016

Published: 03 November 2017 Publication History

Abstract

This paper presents our methods to the Audio-Video Based Emotion Recognition subtask in the 2017 Emotion Recognition in the Wild (EmotiW) Challenge. The task aims to predict one of the seven basic emotions for short video segments. We extract different features from audio and facial expression modalities. We also explore the temporal LSTM model with the input of frame facial features, which improves the performance of the non-temporal model. The fusion of different modality features and the temporal model lead us to achieve a 58.5% accuracy on the testing set, which shows the effectiveness of our methods.

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Cited By

Singh PTripathi MPatil MShivendra Neelakantappa M(2024)Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature setMultimedia Tools and Applications10.1007/s11042-024-19171-2Online publication date: 26-Apr-2024
https://doi.org/10.1007/s11042-024-19171-2
Xu CDu YWang JZheng WLi TYuan Z(2023)A joint hierarchical cross‐attention graph convolutional network for multi‐modal facial expression recognitionComputational Intelligence10.1111/coin.1260740:1Online publication date: 25-Oct-2023
https://doi.org/10.1111/coin.12607
Wang WHuang JWu WZhang JHuang YLi SHe PLyu M(2023)MTTM: Metamorphic Testing for Textual Content Moderation Software2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)10.1109/ICSE48619.2023.00200(2387-2399)Online publication date: May-2023
https://doi.org/10.1109/ICSE48619.2023.00200
Show More Cited By

Index Terms

Emotion recognition with multimodal features and temporal models
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning

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Published In

cover image ACM Conferences

ICMI '17: Proceedings of the 19th ACM International Conference on Multimodal Interaction

November 2017

676 pages

ISBN:9781450355438

DOI:10.1145/3136755

General Chairs:
Edward Lank
University of Waterloo, Canada
,
Alessandro Vinciarelli
University of Glasgow, UK
,
Program Chairs:
Eve Hoggan
Aarhus University, Denmark
,
Sriram Subramanian
University of Sussex, UK
,
Stephen A. Brewster
University of Glasgow, UK

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2017

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ICMI '17

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SIGCHI

ICMI '17: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

November 13 - 17, 2017

Glasgow, UK

Acceptance Rates

ICMI '17 Paper Acceptance Rate 65 of 149 submissions, 44%;

Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Cited By

Singh PTripathi MPatil MShivendra Neelakantappa M(2024)Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature setMultimedia Tools and Applications10.1007/s11042-024-19171-2Online publication date: 26-Apr-2024
https://doi.org/10.1007/s11042-024-19171-2
Xu CDu YWang JZheng WLi TYuan Z(2023)A joint hierarchical cross‐attention graph convolutional network for multi‐modal facial expression recognitionComputational Intelligence10.1111/coin.1260740:1Online publication date: 25-Oct-2023
https://doi.org/10.1111/coin.12607
Wang WHuang JWu WZhang JHuang YLi SHe PLyu M(2023)MTTM: Metamorphic Testing for Textual Content Moderation Software2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)10.1109/ICSE48619.2023.00200(2387-2399)Online publication date: May-2023
https://doi.org/10.1109/ICSE48619.2023.00200
Wang WHuang JHuang JChen CGu JHe PLyu M(2023)An Image is Worth a Thousand Toxic Words: A Metamorphic Testing Framework for Content Moderation Software2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE56229.2023.00189(1339-1351)Online publication date: 11-Sep-2023
https://doi.org/10.1109/ASE56229.2023.00189
Doğdu CKessler TSchneider DShadaydeh MSchweinberger S(2022)A Comparison of Machine Learning Algorithms and Feature Sets for Automatic Vocal Emotion Recognition in SpeechSensors10.3390/s2219756122:19(7561)Online publication date: 6-Oct-2022
https://doi.org/10.3390/s22197561
Wei JYang XDong Y(2021)User-generated video emotion recognition based on key framesMultimedia Tools and Applications10.1007/s11042-020-10203-1Online publication date: 22-Jan-2021
https://doi.org/10.1007/s11042-020-10203-1
Haider FPollak SAlbert PLuz S(2019)Extracting Audio-Visual Features for Emotion Recognition Through Active Feature Selection2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)10.1109/GlobalSIP45357.2019.8969360(1-5)Online publication date: Nov-2019
https://doi.org/10.1109/GlobalSIP45357.2019.8969360

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