Why is facial expression analysis in the wild challenging?
Abstract
In this paper, we discuss the challenges for facial expression analysis in the wild. We studied the problems exemplarily on the Emotion Recognition in the Wild Challenge 2013 [3] dataset. We performed extensive experiments on this dataset comparing different approaches for face alignment, face representation, and classification, as well as human performance. It turns out that under close-to-real conditions, especially with co-occurring speech, it is hard even for humans to assign emotion labels to clips when only taking video into account. Our experiments on automatic emotion classification achieved at best a correct classification rate of 29.81% on the test set using Gabor features and linear support vector machines, which were trained on web images. This result is 7.06% better than the official baseline, which additionally incorporates time information.
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Because of the subtleness of facial configurations, most predesigned representations are dynamically extracting the appearance.
“Because of the subtleness of facial configurations most predesigned representations are dynamically extracting the appearance [262], [263], [264], [265].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [262]
Some approaches to facial expression detection in naturalistic environments use predesigned representations while recent successful approaches learn the best representation or combine predesigned and learned features.
“Some of the approaches use predesigned representations [194], [262], [263], [264], [265] while recent successful approaches learn the best representation [146], [147], [152] or combine predesigned and learned features [108].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [262]
While approaches to facial expression detection in naturalistic environments using static representations exist, dynamic representations are dominant.
“While approaches to FE detection in naturalistic environments using static representations exist [194], [262], dynamic representations are dominant [108], [113], [146], [147], [263], [264].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [262]
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Automatic analysis of facial expressions: the state of the art
Pantie, M., & Rothkrantz, L. J. M. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.895976 · Cited by 1,778 · cited 1× · see citing works
“Research on it already goes back to more than two decades [7, 13, 20, 23].”
BackgroundRecognizing action units for facial expression analysis
Tian, Y.-I., Kanade, T., & Cohn, J. F. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908962 · Cited by 1,745 · see citing works
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Corneanu, C. A., Simon, M. O., Cohn, J. F., & Guerrero, S. E. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2515606 · Cited by 527 · see citing works
- Authors
- Gehrig, T., & Ekenel, H. K.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- ORCID
- Ekenel, Hazım Kemal
- Venue
- Proceedings of the 2013 on Emotion recognition in the wild challenge and workshop
- Publisher
- ACM
- Published
- 2013-12-09
- Pages
- 9-16
- OpenAlex
- W2072728177
- Cited by
- 21
- References
- 2
- Citations by year
- 2024: 2 · 2022: 1 · 2021: 3 · 2020: 1 · 2019: 3 · 2017: 2 · 2016: 5 · 2015: 2 · 2014: 2
- FWCI
- 4.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Istanbul Technical University Academic Open Archive (Istanbul Technical University) (submittedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial expression analysis, facial landmark detection, face representation, Gabor features, support vector machine
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