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The Indonesian Mixed Emotion Dataset (IMED): A Facial Expression Dataset for Mixed Emotion Recognition

Authors:

Dewi Yanti Liliana,

Imelda Ika Dian OrizaAuthors Info & Claims

AIVR 2018: Proceedings of the 2018 International Conference on Artificial Intelligence and Virtual Reality

Pages 56 - 60

https://doi.org/10.1145/3293663.3293671

Published: 23 November 2018 Publication History

Abstract

In Social Signal Processing (SSP) and affective computing area, the publicly available facial expression dataset for emotion recognition task is still limited for basic emotion categories. Whereas in everyday life, various types of emotions are being used by humans more than basic emotion, such as mixed emotion. To enrich the diversity of the existing dataset, we developed the Indonesian Mixed Emotion Dataset (IMED). The objective of creating this dataset is to provide the annotated data for mixed emotion recognition as a ground-truth for benchmarking. Mixed emotion is constructed by combining basic emotion categories to resulting new ones. This dataset can be used to facilitate mixed emotion recognition experiments. Our dataset displays 19 categories of emotions performed by 15 subjects, all are Indonesians with various ethnicities: Javanese, Sundanese, Malay, Bataknese, Minang, and Manadonian. Subjects are 60% female and 40% male with age ranging from 17 to 32 demonstrated basic and mixed emotion classes in videos. We then used a computational model to show that mixed emotion categories were discriminable to be recognized by machine classifiers. We believe that IMED dataset is useful for researchers on the same field to test their novel method by using our dataset.

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Cited By

Iswanto IChowanda ASoeparno HBudiharto W(2024)Implementation of Dynamic Image for Facial Expression Recognition on Indonesian Facial Expression Dataset2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)10.1109/IICAIET62352.2024.10730516(7-12)Online publication date: 26-Aug-2024
https://doi.org/10.1109/IICAIET62352.2024.10730516
Chowanda ANadia Diana (2023)Implementing Vision Transformer to Model Emotions Recognition from Facial Expressions2023 4th International Conference on Artificial Intelligence and Data Sciences (AiDAS)10.1109/AiDAS60501.2023.10284712(48-53)Online publication date: 6-Sep-2023
https://doi.org/10.1109/AiDAS60501.2023.10284712
Rizkinia MFaustine NOkuda M(2022)Conditional Generative Adversarial Networks with Total Variation and Color Correction for Generating Indonesian Face Photo from SketchApplied Sciences10.3390/app12191000612:19(10006)Online publication date: 5-Oct-2022
https://doi.org/10.3390/app121910006
Show More Cited By

Index Terms

The Indonesian Mixed Emotion Dataset (IMED): A Facial Expression Dataset for Mixed Emotion Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding

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AIVR 2018: Proceedings of the 2018 International Conference on Artificial Intelligence and Virtual Reality

November 2018

144 pages

ISBN:9781450366410

DOI:10.1145/3293663

Copyright © 2018 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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University of Tsukuba: University of Tsukuba

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 November 2018

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AIVR 2018

AIVR 2018: 2018 International Conference on Artificial Intelligence and Virtual Reality

November 23 - 25, 2018

Nagoya, Japan

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Cited By

Iswanto IChowanda ASoeparno HBudiharto W(2024)Implementation of Dynamic Image for Facial Expression Recognition on Indonesian Facial Expression Dataset2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)10.1109/IICAIET62352.2024.10730516(7-12)Online publication date: 26-Aug-2024
https://doi.org/10.1109/IICAIET62352.2024.10730516
Chowanda ANadia Diana (2023)Implementing Vision Transformer to Model Emotions Recognition from Facial Expressions2023 4th International Conference on Artificial Intelligence and Data Sciences (AiDAS)10.1109/AiDAS60501.2023.10284712(48-53)Online publication date: 6-Sep-2023
https://doi.org/10.1109/AiDAS60501.2023.10284712
Rizkinia MFaustine NOkuda M(2022)Conditional Generative Adversarial Networks with Total Variation and Color Correction for Generating Indonesian Face Photo from SketchApplied Sciences10.3390/app12191000612:19(10006)Online publication date: 5-Oct-2022
https://doi.org/10.3390/app121910006
Utami PHartanto RSoesanti I(2022)The Development of Facial Expressions Dataset for Teaching Context: Preliminary Research2022 5th International Conference of Computer and Informatics Engineering (IC2IE)10.1109/IC2IE56416.2022.9970043(263-269)Online publication date: 13-Sep-2022
https://doi.org/10.1109/IC2IE56416.2022.9970043
Astudillo GSanz CBaldassarri S(2022)Emotional Meta-annotation of Educational Videos. A Review of Emotional Database CharacterizationApplications and Usability of Interactive TV10.1007/978-3-031-22210-8_4(53-69)Online publication date: 17-Dec-2022
https://doi.org/10.1007/978-3-031-22210-8_4
Chowanda A(2021)Separable convolutional neural networks for facial expressions recognitionJournal of Big Data10.1186/s40537-021-00522-x8:1Online publication date: 16-Oct-2021
https://doi.org/10.1186/s40537-021-00522-x
Carissa Putri WBasaruddin T(2021)Facial Emotion Generation using StarGAN with Differentiable AugmentationProceedings of the 2021 4th International Conference on Signal Processing and Machine Learning10.1145/3483207.3483218(66-71)Online publication date: 18-Aug-2021
https://dl.acm.org/doi/10.1145/3483207.3483218
Afriansyah YNugrahaeni RPrasasti A(2021)Facial Expression Classification for User Experience Testing Using K-Nearest Neighbor2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)10.1109/IAICT52856.2021.9532535(63-68)Online publication date: 27-Jul-2021
https://doi.org/10.1109/IAICT52856.2021.9532535
Ullah STian W(2020)A Systematic Literature Review of Recognition of Compound Facial Expression of EmotionsProceedings of the 2020 4th International Conference on Video and Image Processing10.1145/3447450.3447469(116-121)Online publication date: 25-Dec-2020
https://dl.acm.org/doi/10.1145/3447450.3447469
Nafis AAdni Navastara DYuniarti A(2020)Facial Expression Recognition on Video Data with Various Face Poses Using Deep Learning2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE)10.1109/ICITEE49829.2020.9271740(362-367)Online publication date: 6-Oct-2020
https://doi.org/10.1109/ICITEE49829.2020.9271740
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