Emotion recognition in the wild challenge 2013
Abstract
Emotion recognition is a very active field of research. The Emotion Recognition In The Wild Challenge and Workshop (EmotiW) 2013 Grand Challenge consists of an audio-video based emotion classification challenges, which mimics real-world conditions. Traditionally, emotion recognition has been performed on laboratory controlled data. While undoubtedly worthwhile at the time, such laboratory controlled data poorly represents the environment and conditions faced in real-world situations. The goal of this Grand Challenge is to define a common platform for evaluation of emotion recognition methods in real-world conditions. The database in the 2013 challenge is the Acted Facial Expression in the Wild (AFEW), which has been collected from movies showing close-to-real-world conditions.
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Variation across people in emotion expression complicates generating emotion-specific features.
“The high dimensionality of the data, the non-linear interactions across the modalities along with the fact that the way an emotion is expressed varies across people complicate the process of generating emotion spe- cific features [5],[6].”
From Multimodal emotion recognition using deep learning architectures (2016) · cites this paper as [6]
Non-linear interactions across modalities complicate generating emotion-specific features.
“The high dimensionality of the data, the non-linear interactions across the modalities along with the fact that the way an emotion is expressed varies across people complicate the process of generating emotion spe- cific features [5],[6].”
From Multimodal emotion recognition using deep learning architectures (2016) · cites this paper as [6]
High dimensionality of multimodal data complicates generating emotion-specific features.
“The high dimensionality of the data, the non-linear interactions across the modalities along with the fact that the way an emotion is expressed varies across people complicate the process of generating emotion spe- cific features [5],[6].”
From Multimodal emotion recognition using deep learning architectures (2016) · cites this paper as [6]
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“Acted Facial Expressions In The Wild (AFEW) [8], GENKI [9], Happy People Images (HAPPEI) [8] and Static Facial Expressions In The Wild (SFEW) [10], are recent emotion databases representing real-world scenarios.”
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Video and Image based Emotion Recognition Challenges in the Wild: EmotiW 2015
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- Authors
- Dhall, A., Goecke, R., Joshi, J., Wagner, M., & Gedeon, T.
- Affiliations
- Australian National University (AU)
- University of Canberra (AU)
- Venue
- Proceedings of the 15th ACM on International conference on multimodal interaction
- Publisher
- ACM
- Published
- 2013-12-09
- Pages
- 509-516
- OpenAlex
- W2153597356
- Cited by
- 178
- References
- 4
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 7 · 2023: 10 · 2022: 3 · 2021: 10 · 2020: 16 · 2019: 14 · 2018: 12 · 2017: 24 · 2016: 21 · 2015: 26 · 2014: 17 · 2013: 11
- FWCI
- 34.02
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (publishedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- emotion recognition, real-world environments, societal challenges, evaluation platform
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