The DOI matches its Crossref record; 31 of the 32 references it cites were found in a registry or an index.
Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark
Abstract
Quality data recorded in varied realistic environments is vital for effective human face related research. Currently available datasets for human facial expression analysis have been generated in highly controlled lab environments. We present a new static facial expression database Static Facial Expressions in the Wild (SFEW) extracted from a temporal facial expressions database Acted Facial Expressions in the Wild (AFEW) [9], which we have extracted from movies. In the past, many robust methods have been reported in the literature. However, these methods have been experimented on different databases or using different protocols within the same databases. The lack of a standard protocol makes it difficult to compare systems and acts as a hindrance in the progress of the field. Therefore, we propose a person independent training and testing protocol for expression recognition as part of the BEFIT workshop. Further, we compare our dataset with the JAFFE and Multi-PIE datasets and provide baseline results.
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“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 [194]
In the citing paper
“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 [194]
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1 of the 32 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- dhall, 2011, Acted Facial Expressions in the Wild Database
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Sorted by citations
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2740923 · Cited by 2,338
“A static subset (SFEW [27]) is created by selecting some frames of AFEW.”
BackgroundDeep Facial Expression Recognition: A Survey
Li, S., & Deng, W. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2981446 · Cited by 2,175
“The most commonly used version, SFEW 2.0, has been divided into three sets: Train, Val and Test.”
BackgroundInfluentialFacial Expression Analysis under Partial Occlusion
Zhang, L., Verma, B., Tjondronegoro, D., & Chandran, V. (2018) · ACM Computing Surveys · doi:10.1145/3158369 · Cited by 1,338
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild
Li, S., Deng, W., & Du, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.277 · Cited by 1,470
“0 [7] contains 700 images extracted from movies, and images were labelled by two independent labelers.”
MethodGoing deeper in facial expression recognition using deep neural networks
Mollahosseini, A., Chan, D., & Mahoor, M. H. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477450 · Cited by 1,155
“We evaluate the proposed method on well-known publicly available facial expression databases: CMU MultiPIE [15], MMI [36], Denver Intensity of Spontaneous Facial Actions (DISFA) [29], extended CK+ [27], GEMEPFERA database [5], SFEW [10], and FER2013 [1].”
MethodInfluential
Metadata
- Authors
- Dhall, A., Goecke, R., Lucey, S., & Gedeon, T.
- Affiliations
- Australian National University (AU)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Venue
- 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 2106-2112
- ISSN
- 2473-9936 (ISSN-L)
- OpenAlex
- W2083021723
- Cited by
- 541
- References
- 32
- Citations by year
- 2026: 22 · 2025: 44 · 2024: 61 · 2023: 68 · 2022: 61 · 2021: 60 · 2020: 59 · 2019: 41 · 2018: 35 · 2017: 26 · 2016: 28 · 2015: 16 · 2014: 8 · 2013: 11 · 2012: 1
- FWCI
- 10.17
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Computer Vision Workshops (publishedVersion)
- University of Canberra Research Portal (submittedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › 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 recognition, static facial expressions, benchmark, JAFFE
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