Cited by 541Emotion and Mood RecognitionFace recognition and analysisFace and Expression Recognition

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Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark

Dhall, Abhinav, Goecke, Roland, Lucey, Simon, Gedeon, Tom·2011·2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)

DOI 10.1109/iccvw.2011.6130508

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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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Pranathi, P., Lakshmi, C., & Manne, S. (2021) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac52330.2021.9640733 · Cited by 4
Contextual Emotion Learning Challenge
Shukla, J., Gupta, P., Bera, A., et al. (2021) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg52635.2021.9667034 · Cited by 4
Highly effective end-to-end single-to-multichannel feature fusion and ensemble classification to decode emotional secretes from small-scale spontaneous facial micro-expressions
Islam, M. S., Sang, Y., Mohammed, A. A. Q., Yuan, L., & Lv, J. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101653 · Cited by 6
“…the Leaky ReLU Class Labels CK+(Lucey et al. 2010) MUGFE(Aifanti et al., 2010) OULU-CASIA(Zhao et al. 2011) RAF-DB(Li et al., 2017) SFEW(Dhall et al. 2011) as non-linear activation to retain non-zero values for negative values in the feature maps to alleviate the vanishing gradient…”
Influential
Context Based Facial Expression Analysis in the Wild
Dhall, A. (2013) · doi:10.1109/acii.2013.111 · Cited by 4
“There has been interesting research in the temporal FER [20], [8], [12].”
BackgroundSelf-citation
Opportunities and Pitfalls in Applying Emotion Recognition Software for Persons With a Visual Impairment: Simulated Real Life Conversations
Buimer, H. P., Schellens, R. T. W., Kostelijk, T., Nemri, A., Zhao, Y., van der Geest, T., & Wezel, R. J. A. V. (2019) · JMIR mhealth and uhealth · doi:10.2196/13722 · Cited by 5
“Facial expressions in real life are often much subtler than those found in the validated sets, and lighting conditions also differ between real life and controlled conditions [22,24,25].”
Background
Compact Selective Transformer Based on Information Entropy for Facial Expression Recognition in the Wild
Guo, L., Jin, L., Ma, G., et al. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222376 · Cited by 4
“SFEW dataset [13] consists of static frames collected from movies, including a training set (958 samples), a validation set (436 samples) and a test set (372 samples).”
Background
USTST: unsupervised self-training similarity transfer for cross-domain facial expression recognition
Guo, Z., Wei, B., Liu, J., et al. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17317-2 · Cited by 4
“We use the RAF-DB [17] dataset as the source domain and the CK+ [11], JAFFE [12, 13], SFEW [14], FER2013 [15] and ExpW [16] datasets as the target domain.”
EC-RFERNet: an edge computing-oriented real-time facial expression recognition network
Sun, Q., Chen, Y., Yang, D., Wen, J., Yang, J., & Li, Y. (2023) · Signal Image and Video Processing · doi:10.1007/s11760-023-02832-4 · Cited by 4
“In this section, this paper ﬁrst introduces the ﬁve datasets, including RAF-DB [31], FER2013, AffectNet [32], CK+ [33], and SFEW [34]”
Facial Expression Recognition Based on Multi-dataset Neural Network
Yang, B., Li, Z., & Cao, E. (2020) · Radioengineering · doi:10.13164/re.2020.0259 · Cited by 4
“Recently, a four-task network was proposed to detect human face [21].”
BackgroundMethod
Emotion Recognition Through Observer's Physiological SignalsPreprint
Liu, Y., Gedeon, T., Caldwell, S., Lin, S., & Jin, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.08034 · Cited by 8
“The dataset is collected by E4 wristband and Eye Tribe while asking subjects to observe the movie clips from AFEW [1].”
BackgroundMethodInfluentialSelf-citation
Pose-aware Adversarial Domain Adaptation for Personalized Facial Expression RecognitionPreprint
Liang, G., Wang, S., & Wang, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.05932 · Cited by 4
“…are collected in controlled environments, to further demonstrate the effectiveness of the proposed method under real conditions, we also conduct within-database experiments on a large in-the-wild dataset, AffectNet [14] and cross-database experiments on a small in-the-wild dataset, SFEW [2].”
Method
AU-Guided Unsupervised Domain Adaptive Facial Expression RecognitionPreprint
Wang, K., Gu, Y., Peng, X., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.10078 · Cited by 4
“Both in industry and academy areas, in the past decades, many well-labeled datasets [27, 38, 51, 10, 3, 32, 11, 23] and high-performance algorithms [41, 24, 40] have been proposed to automatically recognize facial expressions in the past decades.”
Background
SWP: A Stage-Weighted Pooling Based Convolutional Neural Networks for Facial Expression Recognition in the Wild
Liu, K.-H., Liu, W.-R., & Liu, T.-J. (2024) · Machine learning for signal processing ... · doi:10.1109/mlsp58920.2024.10734814 · Cited by 4
An Enhanced Adaptive Confidence Margin for Semi-Supervised Facial Expression Recognition
Li, H., Wang, N., Yang, X., Wang, X., & Gao, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3612953 · Cited by 4
“(cid:2) SFEW [55] is a static facial expression dataset selected from movies, including 958 images for training, 436 images for validation, and 372 images for testing.”
Emotion Recognition Using Temporal Facial Skin Temperature and Eye-Opening Degree During Digital Content Viewing for Japanese Older Adults
Tanabe, R., Kikuchi, R., Zou, M., Suehiro, K., Takahashi, N., Saito, H., Kobayashi, T., Satake, H., Sato, N., & Kageyama, Y. (2025) · Sensors · doi:10.3390/s25216545 · Cited by 4
“The method includes the use of the FER2013 [20] and SFEW [21] datasets, which contain facial expressions triggered by videos.”
Data Imbalance Handling in Facial Expression Recognition: A Systematic Literature Review
Hunafa, M. H., Ramadhan, A. W., Kushirayati, S., et al. (2026) · IEEE Access · doi:10.1109/access.2026.3653062 · Cited by 3
“The Static Facial Expressions in the Wild (SFEW) [47] dataset is a dataset for facial expression recognition.”
UIBVFEDPlus-Light: Virtual facial expression dataset with lighting
Mascaró-Oliver, M., Amengual, E., Roig-Maimó, M. F., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0287006 · Cited by 3
“The second group are databases generated in an uncontrolled environment, such as the Static Facial Expression in the Wild (SFEW) [5] or AffectNet [6].”
Background
Facial Expression Retargeting from a Single Character
Larey, A., Asraf, O., Kelder, A., et al. (2024) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-9018-4_16 · Cited by 3
Queue-Augmented Correlation-Biased Orthogonality Loss and Implicit Selective Transformer for Facial Expression Recognition in the Wild
Guo, L., Jin, L., & Song, E. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3578624 · Cited by 3
“SFEW [43] images.”
Dual-Branch Multi-Dimensional Attention Mechanism for Joint Facial Expression Detection and Classification
Peng, C., Li, B., Zou, K., Zhang, B., Dai, G., & Tsoi, A. C. (2025) · Sensors · doi:10.3390/s25123815 · Cited by 3
Towards Fault-Aware Image Captioning: A Review on Integrating Facial Expression Recognition (FER) and Object Detection
Khan, A. S., Abbass, M. J., & Khan, A. (2025) · Sensors · doi:10.3390/s25195992 · Cited by 5
An approach to facial expression analysis with multi-model interactions
Xu, C., Hu, Q., Xu, G., & Feng, Z. (2013) · International Journal of Computer Mathematics · doi:10.1080/00207160.2013.802309 · Cited by 3
“Person-independent facial expression space was proposed based on supervised locality preserving projections on a generalized manifold [24], and person-independent training and testing evaluation protocol were also proposed for tough conditions [3].”
Method
Human-robot interaction assessment using dynamic engagement profiles
Poltorak, N., & Drimus, A. M. (2017) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2017.8246941 · Cited by 3
“The SFEW dataset[16] consists of real world photos”
Background
Measuring Observers' EDA Responses to Emotional Videos
Rahman, J. S., Hossain, M. Z., & Gedeon, T. (2019) · Australian Conference on Human-Computer Interaction (OzCHI) · doi:10.1145/3369457.3369516 · Cited by 3
“The Acted Facial Expressions In TheWild (AFEW) dataset [3] has been used for the purpose of this experiment.”
MethodSelf-citation
Face Value: On the Impact of Annotation (In-)Consistencies and Label Ambiguity in Facial Data on Emotion Recognition
Gebele, J., Brüne, P., & Faußer, S. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956230 · Cited by 3
Survey on Artificial Intelligence Algorithms Application for Alzheimer’s and Elderly People Safety in Smart Homes
Benlala, W., Bouchelaghem, S., & Yazid, M. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-21216-1_42 · Cited by 3
Comparative studies of facial emotion detection in online learning
Ahmad, A. S., hassan, R. B., Zakaria, N. H., & Moi, S. H. (2023) · AIP conference proceedings · doi:10.1063/5.0164746 · Cited by 3
Unimodal Sentiment Analysis
Xu, H. (2023) · Springer eBooks · doi:10.1007/978-981-99-5776-7_4 · Cited by 3
Emotion Recognition from Facial Expressions using Deep Recurrent Attention Network
Gahlan, N., & Sethia, D. (2024) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets59351.2024.10427068 · Cited by 4
“Notable datasets in this field encompass JAFFE [3], Cohn-Kanade Dataset (CK) [4], Extended Cohn-Kanade Dataset (CK+) [5], KDEF [6], AffectNet [7], Static Facial Expression in the Wild (SFEW) [8], and FER2013 [9].”
BackgroundMethod
A New Joint Training Method for Facial Expression Recognition with Inconsistently Annotated and Imbalanced Data
Chen, T., Zhang, D., & Lee, D.-J. (2024) · Electronics · doi:10.3390/electronics13193891 · Cited by 5
DSAP: Analyzing bias through demographic comparison of datasets
Dominguez-Catena, I., Paternain, D., & Galar, M. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102760 · Cited by 10

Authors: Dhall, A., Goecke, R., Lucey, S., & Gedeon, T.
Affiliations: Australian National University (AU)
Commonwealth Scientific and Industrial Research Organisation (AU)
ORCID: Dhall, Abhinav
Goecke, Roland
Lucey, Simon
Gedeon, Tom
Venue: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
Publisher: IEEE
Published: 2011-11
Pages: 2106-2112
ISSN: 2473-9936 (ISSN-L)
DOI: 10.1109/iccvw.2011.6130508
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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Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark

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