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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]
Citations
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Rapid object detection using a boosted cascade of simple features
Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,123
Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
Huang, G. B., Mattar, M., Berg, T. L., & Miller, E. G. (2008) · HAL · Cited by 4,445
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797
Coding facial expressions with Gabor wavelets
Lyons, M. J., Akamatsu, S., Kamachi, M., & Gyoba, J. (2002) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.1998.670949 · Cited by 2,214
The CMU pose, illumination, and expression database
Sim, T., Baker, S., & Bsat, M. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1251154 · Cited by 1,786
Representing shape with a spatial pyramid kernel
Bosch, A., Zisserman, A., & Muñoz, X. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282340 · Cited by 1,423
Research works printed but not linked: 1
- dhall, 2011, Acted Facial Expressions in the Wild Database
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Sorted by citations
Items 201–300 of 541
Lee, J. R. H., & Wong, A. K. C. (2020) · Conference on Computer and Robot Vision (CRV) · doi:10.1109/crv50864.2020.00010 · Cited by 22
“A subset of this, the Static Facial Expressions in the Wild (SFEW) [23] dataset was also made, created from extracting static frames in AFEW.”
MethodGuthier, B., Dörner, R., & Martínez, H. P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46152-6_16 · Cited by 23
GCANet: Geometry cues-aware facial expression recognition based on graph convolutional networks
Wang, S., Zhao, A., Lai, C., Zhang, Q., Li, D., Gao, Y., Dong, L., & Wang, X. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101605 · Cited by 22
“0 (Dhall et al., 2011) is built from the AFEW database by selecting key frames, which contains 879 training face samples, 406 validation instances as well as 372 testing images.”
Zou, X., Yan, Y., Xue, J., Chen, S., & Wang, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19800-7_40 · Cited by 21
Yu, C., Zhang, D., Zou, W. H., & Li, M. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3382618 · Cited by 21
“groupedintolab-controlledFERdatasets[1],[2],[3],[4],[5]and in-the-wildFERdatasets[6],[7],[8],[9],[10],[11].”
Pose-robust feature learning for facial expression recognition
Zhang, F., Yu, Y., Mao, Q., et al. (2016) · Frontiers of Computer Science · doi:10.1007/s11704-015-5323-3 · Cited by 21
Affective facial computing: Generalizability across domains
Cohn, J. F., Ertuğrul, I. Ö., Chu, W.-S., Girard, J. M., Jeni, L. A., & Hammal, Z. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-814601-9.00026-2 · Cited by 22
Surrogate network-based sparseness hyper-parameter optimization for deep expression recognition
Xie, W., Chen, W., Shen, L., Duan, J., & Yang, M. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107701 · Cited by 21
“0) [25] is a dynamic temporal facial expressions data corpus extracted from movies, where 681, 76 and 365 of the 1122 sequences are used for training, validation and testing, respectively.”
MethodFacial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey
Shangguan, Z., Dong, Y., Guo, S., Leung, V. C. M., Deen, M. J., & Hu, X. (2025) · ACM Computing Surveys · doi:10.1145/3737456 · Cited by 22
“A neuropsychological perspective was provided on the distinction between MiEs and MaEs, i.e., the greater inhibitory effect of MiE on facial expressions in [10].”
Uncertainty-aware Cross-dataset Facial Expression Recognition via Regularized Conditional Alignment
Zhou, L., Fan, X., Ma, Y., Tjahjadi, T., & Ye, Q. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413515 · Cited by 20
“The SFEW 2.0 database was created from Acted Facial Expressions in the Wild (AFEW) [6] using the key-frame extraction method.”
MethodInfluentialFacial Expression Recognition in the Wild
Zhang, F., Zhang, T., Mao, Q., Duan, L., & Xu, C. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240574 · Cited by 20
“Comparison results on the SFEW: We compare our method with the current state-of-the-art results reported in [10] including MvDA, GMLDA, GMLPP, DS-GPLVM, and the baseline designed by the dataset creators [8] on the SFEW.”
BackgroundMethodResultInfluentialD³Net: Dual-Branch Disturbance Disentangling Network for Facial Expression Recognition
Mo, R., Yan, Y., Xue, J., Chen, S., & Wang, H. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475249 · Cited by 19
“SFEW.”
MethodInfluentialLi, M., Li, X., Sun, W. M., Wang, X., & Wang, S. (2021) · Journal of Real-Time Image Processing · doi:10.1007/s11554-021-01088-w · Cited by 18
“0[15], etc.”
Confusable facial expression recognition with geometry-aware conditional network
Liu, T., Li, J., Wu, J. X., et al. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110174 · Cited by 18
Wang, S., Chang, Y., Li, Q., et al. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110311 · Cited by 18
DeepEmo: Real-world facial expression analysis via deep learning
Deng, W., Hu, J., Zhang, S., et al. (2015) · Visual Communications and Image Processing · doi:10.1109/vcip.2015.7457876 · Cited by 18
“Two representative static image datasets are GENKI-4K [7] and SFEW [8], which are less constrained by making use of the realistic data on the web, but their size is limited for real-world emotion recognition (e.g. SFEW contains only 700 images, and GENKI-4K has 4000 images with only ‘smile’ or ‘non-smile’ labels).”
MethodA Three-stage multimodal emotion recognition network based on text low-rank fusion
Zhao, L., Yang, Y., & Ning, T. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01345-5 · Cited by 21
“…of the early datasets contain only audio and visual modalities, such as Bahçeşehir University Multimodal Affective Database -1 (BAUM-1) [18], the Acted Facial Expressions In The Wild (AFEW) [19], and Sustained Emotionally Coloured Machine-human Interaction using Nonverbal Expression (SEMAINE) [20].”
Quaternion Convolutional Neural Networks: Current Advances and Future Directions
Altamirano-Gómez, G., & Gershenson, C. (2024) · Advances in Applied Clifford Algebras · doi:10.1007/s00006-024-01350-x · Cited by 22
“They apply it on a facial recognition task using the Oulu-CASIA [123], MMI [124], and SFEW [125] datasets.”
BackgroundMethodRegion-based feature fusion for facial-expression recognition
Turan, C., & Lam, K. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7026204 · Cited by 17
Emotion recognition in the wild
Dhall, A., Goecke, R., Gedeon, T., & Sebe, N. (2016) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-016-0213-z · Cited by 17
“DuringEmotiW2015 [5], a newsub-challenge—image based static facial expression recognition—was introduced, which was based on the Static Facial Expressions in theWild (SFEW)database [3].”
MethodSelf-citationHierarchical Group-level Emotion Recognition in the Wild
Fujii, K., Sugimura, D., & Hamamoto, T. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756573 · Cited by 17
“from social psychology [21]; they have also been utilized in previous studies [11], [16], [24], [25].”
MethodFeature Guided CNN for Baby’s Facial Expression Recognition
Lin, Q., He, R., & Jiang, P. (2020) · Complexity · doi:10.1155/2020/8855885 · Cited by 17
“Most of the existing works and datasets [7, 8] focus on analyzing adult faces, which ignore how to analyze facial expressions from baby facial images.”
BackgroundFacial Expression Recognition Based on Transfer Learning and SVM
Yang, L., Zhang, H., Li, D., et al. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2025/1/012015 · Cited by 17
“SFEW [9] and FER2013 are wild environment data.”
BackgroundInfluentialData Augmentation and Second-Order Pooling for Facial Expression Recognition
Tong, X., Sun, S., & Fu, M. (2019) · IEEE Access · doi:10.1109/access.2019.2923530 · Cited by 17
“SFEW [20] extracted from a temporal facial expressions database Acted Facial Expressions in theWild which we have extracted from movies.”
MethodOrtmann, T., Wang, Q., & Putzar, L. (2024) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii63134.2024.00014 · Cited by 16
“We combined data from the AffectNet, EmotioNet [33], ExpW [34], FER+, and SFEW [35] datasets for the seven emotion categories used in EmojiHeroVR.”
Non‐linear dimensionality reduction using fuzzy lattices
Kapoor, R., & Gupta, R. (2013) · IET Computer Vision · doi:10.1049/iet-cvi.2012.0097 · Cited by 16
Mood-aware visual question answering
Ruwa, N., Mao, Q., Wang, L., Gou, J., & Dong, M. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.11.049 · Cited by 16
Facial feature tracking: a psychophysiological measure to assess exercise intensity?
Miles, K. H., Clark, B., Périard, J. D., et al. (2017) · Journal of Sports Sciences · doi:10.1080/02640414.2017.1346275 · Cited by 16
“Facial expression reflects internal affect (Dhall et al., 2011); however, facial expressions of affect tend to be different among individuals.”
BackgroundInfluentialSelf-citationDu, L., & Hu, H. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.06.003 · Cited by 17
The Florence 4D Facial Expression Dataset
Principi, F., Berretti, S., Ferrari, C., et al. (2023) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg57933.2023.10042606 · Cited by 15
“(not posed) datasets, with some examples of in-the-wild acquisitions [9], [14].”
BackgroundBias-Based Soft Label Learning for Facial Expression Recognition
Wang, S., Shuai, H., Liu, C., & Liu, Q. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3220291 · Cited by 16
“We verify the effectiveness of EC-Net on four popular benchmarks, including RAF-DB [1], AffectNet [2], SFEW [48] and CAER-S [49].”
BackgroundMethodPIDViT: Pose-Invariant Distilled Vision Transformer for Facial Expression Recognition in the Wild
Huang, Y., & Tsai, C. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3220972 · Cited by 15
“0) dataset [12].”
Noisy label facial expression recognition via face-specific label distribution learning
Shin, H., Lee, B., Ku, B., & Ko, H. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.104901 · Cited by 16
Delving Into Quaternion Wavelet Transformer for Facial Expression Recognition in the Wild
Zhou, Y., Pei, J., Si, W., et al. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3535361 · Cited by 15
“We evaluate our model by four well-known FER in-the-wild datasets, including SFEW [66], RAF-DB [67], AffectNet [68], and ExpW [69].”
Analyzing Group-Level Emotion with Global Alignment Kernel based Approach
Huang, X., Dhall, A., Goecke, R., Pietikäinen, M., & Zhao, G. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2953664 · Cited by 15
“by [10], [11], [14], [15], we are mostly concerned with three tasks in group-level emotion recognition: group-level happiness intensity estimation [15], group-level valence and arousal prediction [10], and group-level facial expression recognition [11].”
BackgroundSelf-citationFacial Emotion Recognition with Noisy Multi-task Annotations
Zhang, S., Huang, Z., Paudel, D. P., & Gool, L. V. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00007 · Cited by 15
“To automatically recognize facial expressions of emotions, large amounts of datasets (e.g., [30, 52, 62, 6, 11, 36, 27, 23, 24]) and machine learning methods (e.g., [58, 45, 37, 3, 29, 20, 64, 57, 28]) have been suggested.”
Kalsum, T., Mehmood, Z., Kulsoom, F., Chaudhry, H. N., Rehman, A. U., Rashid, M., & Saba, T. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-201799 · Cited by 17
“We evaluated the performance of the proposed FERS model based on realistic datasets i.e. SFEW [52], FER-2013 [53], RaF-DB [54] and also based on those collected in lab constrained environment i.e. TFEID [55], JAFFE [56], and CK+ [57].”
An Out-of-Distribution Attack Resistance Approach to Emotion Categorization
Shehu, H. A., Browne, W. N., & Eisenbarth, H. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3105371 · Cited by 15
Facial Expression-Aware Face Frontalization
Wang, Y., Yu, H. L., Dong, J., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54187-7_25 · Cited by 15
“Considering SFEW is a small-scale database, the result of this method is consequently unstable.”
BackgroundMethodInfluentialAutomatic Smile Detection of Infants in Mother-Infant Interaction via CNN-based Feature Learning
Tang, C., Zheng, W., Zong, Y., et al. (2018) · doi:10.1145/3267935.3267951 · Cited by 15
“As for RGB signal based datasets, the following datasets: JAFFE [23], FER2013 [2], AFEW [6], SFEW [5], RAF-DB [17], EmotioNet [9], AffectNet [24], CK+ [22], MMI [25], Oulu-CASIA [34] are extensively investigated.”
MethodD’Amelio, T. A., Galán, L. A., Maldonado, E. A., Barquinero, A. D., Cuello, J. R., Bruno, N. M., Tagliazucchi, E., & Engemann, D. A. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130831 · Cited by 20
Heuristic objective for facial expression recognition
Li, H., Xiao, X., Liu, X., Guo, J., Wen, G., & Liang, P. (2022) · The Visual Computer · doi:10.1007/s00371-022-02619-7 · Cited by 14
“Recently, with the largescale databases (AffectNet [2], RAF-DB [3], SFEW [4], FER2013 [5], FER2013plus [6], EmotionNet [7], etc.”
Huang, P.-J., Xie, H., Huang, H.-C., Shuai, H.-H., & Cheng, W.-H. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3312768 · Cited by 14
“SFEW 2.0 [59] is a dataset containing static frames selected from the Acted Facial Expression in the Wild [60].”
InfluentialThree-Dimensional View Relationship-Based Context-Aware Emotion Recognition
Zhang, L., Zheng, X., Chen, X., & Cui, L. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3476249 · Cited by 14
“The static facial expression dataset in film named SFEW [49] is employed in the process of pretraining.”
InfluentialShehu, H. A., Browne, W. N., & Eisenbarth, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129367 · Cited by 24
Transductive Transfer LDA with Riesz-based Volume LBP for Emotion Recognition in The Wild
Zong, Y., Zheng, W., Huang, X., et al. (2015) · ACM International Conference on Multimodal Interaction (ICMI) · doi:10.1145/2818346.2830584 · Cited by 14
“In order to make TTLDA work efficiently and obtain a satisfied challenge result, we use the following strategy to train our TTLDA: (1) We firstly employ RVLBP for each face image on SFEW 1.0 [4] and SFEW 2.0 databases.”
MethodInfluentialDeep Feature Selection for Facial Emotion Recognition Based on BPSO and SVM
DONUK, K., Arı, A., ÖZDEMİR, M. F., & Hanbay, D. (2021) · Journal of Polytechnic · doi:10.2339/politeknik.992720 · Cited by 14
Real world expression recognition: A highly imbalanced detection problem
Li, S., & Deng, W. (2016) · International Conference on Biometrics · doi:10.1109/icb.2016.7550074 · Cited by 13
“In AFEW [8], the final distribution of emotion classes of the sequences is slightly imbalanced, but it is not discussed in the paper.”
BackgroundShe, J., Hu, Y., Shi, H., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00232 · Cited by 14
“SFEW [12] contains the images from movies with seven basic emotions, including 958 images for training and 436 images for testing.”
BackgroundBoughanem, H., Ghazouani, H., & Barhoumi, W. (2022) · The Visual Computer · doi:10.1007/s00371-022-02690-0 · Cited by 14
“It has been extracted from the temporal dataset Acted Facial Expressions in the Wild (AFEW) and was firstly published in 2011 by Dhall et al. [71].”
MethodHow You Feelin’? Learning Emotions and Mental States in Movie Scenes
Srivastava, D., Singh, A. K., & Tapaswi, M. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00248 · Cited by 12
“When not mentioned, we adopt the defaults: (i) MViT trained on Ki-netics400 dataset to represent video; (ii) ResNet50 trained on SFEW, FER, and VGGFace for character representations; (iii) fine-tuned RoBERTa for dialog utterance representations; and (iv) EmoTx with appropriate masking to pick modalities or change the…”
MethodCross-Domain Sample Relationship Learning for Facial Expression Recognition
Chen, D., Wen, G., Wen, P., et al. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3316027 · Cited by 13
“Generally, they are based on well-labeled databases including laboratory-controlled databases [11], [12], [13] and real-world databases [6], [14], [15], [16], having the ideal assumption that both training and test data come from a common single database and follow an identical distribution.”
BackgroundHard Sample-aware Consistency for Low-resolution Facial Expression Recognition
Lee, B., Ko, K., Hong, J., & Ko, H. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00027 · Cited by 14
“0 [4] in our experiments, which are “in-the-wild” datasets.”
BackgroundHuman Emotion: A Survey focusing on Languages, Ontologies, Datasets, and Systems
Elkobaisi, M. R., Machot, F. A., & Mayr, H. C. (2022) · SN Computer Science · doi:10.1007/s42979-022-01116-x · Cited by 18
“SFEW Static Facial Expressions in the Wild [132] have been created by selecting frames from the AFEW [131] data-set.”
BackgroundMethodSaurav, S., Saini, A. K., Saini, R., & Singh, S. K. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06613-3 · Cited by 13
“The SFEW dataset contains facial images generated from the video sequences of the Acted Facial Expressions in the Wild (AFEW) dataset [69].”
MethodPrivacy-Preserving Deep Inference for Rich User Data on The CloudPreprint
Osia, S. A., Shamsabadi, A. S., Taheri, A., Katevas, K., Rabiee, H. R., Lane, N. D., & Haddadi, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.01727 · Cited by 17
“Static Facial Expression in the Wild (SFEW) is an emotion detection benchmark [29].”
BackgroundN, N. K. H., Patil, C. M., Jain, A. K., Sudheesh, K. V., & Mahadevaswamy (2022) · International Conference on Cognitive Computing and Information Processing · doi:10.1109/ccip57447.2022.10058627 · Cited by 12
“M) Wild setting SFEW [18] 330` 1-70 - - 7 Wild setting W: Women, M: Men, C: Children, Differing ethnicity: Latino/Caucasian/ African/Hispanic/American /Asian/Black III.”
Du, Z., Jiang, X., Wang, P. W., Zhou, Q., Wu, X., Zhou, J., & Wang, Y. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/78 · Cited by 13
“We verify the effectiveness of our LION model on three datasets: RAF-DB [Li et al ., 2017], AffectNet [Mol-lahosseini et al ., 2017], and SFEW [Dhall et al., 2011].”
MethodGao, Y., Cai, Y., Bi, X., et al. (2023) · Electronics · doi:10.3390/electronics12214553 · Cited by 12
Kumar, S., Bhuyan, M. K., & Iwahori, Y. (2020) · The Visual Computer · doi:10.1007/s00371-019-01788-2 · Cited by 12
“Experiments on KDEF dataset Images of BU3DFE dataset are synthetic, so we validate our proposed method by the images of multi-view KDEF dataset [45], and also by the dataset formed by combining images of both BU3DFE and KDEF datasets.”
MethodContinuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks
Teixeira, T., Granger, É., & Koerich, A. L. (2021) · Applied Sciences · doi:10.3390/app112411738 · Cited by 12
“, 2010), and SFEW (Dhall et al., 2011) datasets were notably obtained using Convolutional Neural Networks (CNNs) (Georgescu et al.”
MethodCross-Domain Facial Expression Recognition via Disentangling Identity Representation
Liu, T., Li, J., Wu, J. X., Zhang, L., Zhao, S., Chang, J., & Wan, J. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/135 · Cited by 13
“0 [Dhall et al., 2011]: The in-the-wild database SFEW 2.”
BackgroundInfluentialJin, L., Zhou, Y., Ma, G., et al. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3305616 · Cited by 12
“SFEW dataset [58] is collected from different movies, which has been predivided into 958 images for training, 436 images for validation and 372 images for testing.”
MethodBi-Model Emotional AI for Audio-Visual Human Emotion Detection Using Hybrid Deep Learning Model
Haldorai, A., Babitha Lincy, R., Murugan, S., & Balakrishnan, M. (2024) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-031-53972-5_15 · Cited by 12
Model-based approach for high-dimensional non-Gaussian visual data clustering and feature weighting
Elguebaly, T., & Bouguila, N. (2015) · Digital Signal Processing · doi:10.1016/j.dsp.2015.02.014 · Cited by 12
Learning Consistent Global-Local Representation for Cross-Domain Facial Expression Recognition
Xie, Y., Gao, Y., Lin, J., et al. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956069 · Cited by 12
“0 [6] is a static in-the-wild expression dataset that covers unconstrained facial expressions, including different head poses, a large age ranges, occlusions, varied focus and different resolutions of faces.”
BackgroundEntity-Based Opinion Mining from Text and Multimedia
Maynard, D., & Hare, J. S. (2015) · Studies in computational intelligence · doi:10.1007/978-3-319-18458-6_4 · Cited by 13
“As this is a rapidly moving area of research, it will be interesting to see how expression modelling techniques develop over the coming years, especially in the presence of benchmarks such as Facial Expressions in the Wild2 [37].”
BackgroundCSE-GResNet: A Simple and Highly Efficient Network for Facial Expression Recognition
Jiang, S., Xing, X., Liu, F., Xu, X., Wang, L., & Guo, K. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3535811 · Cited by 12
Semi-Supervised Facial Expression Recognition by Exploring False Pseudo-Labels
Sun, H., Pi, C., & Xie, W. C. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00048 · Cited by 11
“In this subsection, ablation experiments on RAF-DB and SFEW datasets are performed to study the performance of these two parts.”
BackgroundInfluentialA comprehensive survey of unimodal facial databases in 2D and 3D domains
Sadhya, D., & Singh, S. K. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.05.045 · Cited by 11
An Efficient Algorithm of Facial Expression Recognition by TSG-RNN Network
Huang, K., Li, J., Cheng, S., Yu, J., Tian, W., Zhao, L., Hu, J., & Chang, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-37734-2_14 · Cited by 11
Multi-level spatial and semantic enhancement network for expression recognition
Ma, Y., Wang, X., & Wei, L. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02254-0 · Cited by 11
“Data imbalance is a ubiquitous problem in most facial expression datasets, e.g., FER-2013 [10], RAF-DB [2] and SFEW [26].”
BackgroundA Summarization of Image and Video Databases for Emotion Recognition
Ashraf, A., Gunawan, T. S., Rahman, F. D. A., & Kartiwi, M. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-33-4597-3_60 · Cited by 11
AU-Guided Unsupervised Domain-Adaptive Facial Expression Recognition
Peng, X., Gu, Y., & Zhang, P. (2022) · Applied Sciences · doi:10.3390/app12094366 · Cited by 16
Chen, T., Pu, T., Wu, H., Xie, Y., Liu, L., & Lin, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.00923 · Cited by 11
“0 [11] is an in-the-wild dataset collected from different films with spontaneous expressions, various head poses, age ranges, occlusions and illuminations.”
BackgroundDual-Branch Multimodal Fusion Network for Driver Facial Emotion Recognition
Wang, L., Chang, Y., & Wang, K. (2024) · Applied Sciences · doi:10.3390/app14209430 · Cited by 10
INDReview on Facial Expression Analysis and its Application in Education
Li, Q., Liu, X., Gong, X., et al. (2019) · Chinese Automation Congress · doi:10.1109/cac48633.2019.8996796 · Cited by 10
The Rhythm of Flow: Detecting Facial Expressions of Flow Experiences Using CNNs
Humpe, L., Murillo, S. F., Muires, J., & Gloor, P. A. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3087222 · Cited by 10
Needle in a Haystack: Spotting and recognising micro-expressions “in the wild”
Gan, Y. S., See, J., Khor, H.-Q., Liu, K., & Liong, S. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.101 · Cited by 10
Deep Learning for Emotion Recognition
Kavitha, T., Malini, S., & Senbagavalli, G. (2022) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-8892-5.ch005 · Cited by 10
Advances in Social Media Analysis
Gaber, M. M., Cocea, M., Wiratunga, N., & Göker, A. (2015) · Studies in computational intelligence · doi:10.1007/978-3-319-18458-6 · Cited by 10
Deep Convolutional Neural Network for Facial Expression Recognition
Zhai, Y., Liu, J., Zeng, J., Piuri, V., Scotti, F., Ying, Z., Xu, Y. W., & Gan, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71607-7_19 · Cited by 11
Recognizing Emotion in the Wild using Multimodal Data
Srivastava, S., Aathreya, S., Hinduja, S., Jannat, S. R., Elhamdadi, H., & Canavan, S. J. (2020) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3382507.3417970 · Cited by 10
Saurav, S., Saini, R., & Singh, S. K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19012-2 · Cited by 10
“In this work, we considered three widely used FER databases, namely the FER2013 [86], RAF-DB [87], and SFEW [88], to evaluate the proposed DCNNs.”
Peng, C., Sun, M., Zou, K., Zhang, B., Dai, G., & Tsoi, A. C. (2024) · Sensors · doi:10.3390/s24216912 · Cited by 9
“In our research, we employed two datasets, RAF-DB (Real World Affective Faces Database) [35] and SFEW (Static Facial Expression in the Wild) [36], to train and assess our FER-YOLO-NCAMamba system.”
MethodAffective Computing Databases: In-Depth Analysis of Systematic Reviews and Surveys
Vaz, P. J., Rodrigues, J. M. F., & Cardoso, P. J. S. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3507289 · Cited by 11
Unsupervised Cross-Domain Facial Expression Recognition via Class Adaptive Self-Training
Wang, S., & Liu, Q. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3529947 · Cited by 11
“0 [58] is an in-the-wild database, collected from various films with spontaneous expressions and diverse head poses.”
Facial Expression Recognition With Heatmap Neighbor Contrastive Learning
Liu, T., Li, J., Wu, J. X., Du, B., Zhan, Y., Tao, D., & Wan, J. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3543029 · Cited by 10
“SFEW [50] is a facial expression dataset created by selecting static frames from AFEW [51].”
Vala, J., & Jaliya, U. K. (2022) · International Journal of Modern Education and Computer Science · doi:10.5815/ijmecs.2022.04.06 · Cited by 10
“Different studies presented various methods for emotion recognition that is depends on correlation among responses of bio-signal as well as basic emotions, like anger, fear, despair, sadness, happiness, and joy [3,4,5,6,7].”
BackgroundAccurate and Robust Eye Center Localization by Deep Voting
Mi, J., Gao, Y., Yuan, S., & Li, W. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3239381 · Cited by 9
A Prototype-Oriented Contrastive Adaption Network for Cross-Domain Facial Expression Recognition
Wang, C., Ding, J., Yan, H., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26284-5_20 · Cited by 9
“0 [5]: The Static Facial Expressions in the Wild(SFEW) 2.0 is a real-world facial expression dataset that was created by selecting static frames from different films with spontaneous expressions, various head pose, age range, occlusions, and illuminations.”
BackgroundInfluentialVentral-Dorsal Attention Capsule Network for facial expression recognition
Qian, Z., Mu, J., & Tian, F. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.103978 · Cited by 9
Local saliency-inspired binary patterns for automatic recognition of multi-view facial expression
Santra, B., & Mukherjee, D. P. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532432 · Cited by 9
“Index Terms— Local Saliency-inspired Binary Pattern (LSiBP), Local Binary Pattern (LBP), multi-view facial expression , KDEF, SFEW, LFPW, structure tensor, saliency likelihoods.”
BackgroundResultInfluentialCombined center dispersion loss function for deep facial expression recognition
Nanda, A., Im, W., Choi, K., & Yang, H. S. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.11.002 · Cited by 9
ARM: a lightweight module to amend facial expression representation
Shi, J., Zhu, S., Wang, D., & Liang, Z. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02339-4 · Cited by 11
“Figure 6 shows the confusion matrixes of the proposed basedResNet-18ARMonRAF-DB,AffectNet (7cls&8cls), andSFEW,whereSU,FE,DI,HA,SA,AN, andNE represent surprise, fear, disgust, happiness, sadness, anger, and neutral, respectively.”
BackgroundInfluentialDeep Multi-Facial Patches Aggregation Network For Facial Expression RecognitionPreprint
Hazourli, A. R., Djeghri, A., Salam, H., & Othmani, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.09298 · Cited by 9
“: With TF based data augmentation [Experiment #4] : With cGAN and TF data augmentation – Generalization power of the MFP-CNN model: – Train on CK+ and test on JAFFE – Train on CK+ and test on SFEW”
BackgroundInfluentialMaurya, V., Awasthi, M. A., Singh, R. K., Maurya, D., & Dwivedi, M. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.51265 · Cited by 8
Yang, Y., Shin, A., Kim, N., Woo, H., Chung, J. J. Y., & Song, J. Y. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642880 · Cited by 9
“During the last decades, machine learning researchers have made promising progress in building datasets and developing AI models for FER [18, 29].”
BackgroundFacial Affective Behavior Analysis with Instruction Tuning
Li, Y., Dao, A., Bao, W., Tan, Z., Chen, T., Liu, H., & Kong, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72649-1_10 · Cited by 18
Cao, X., Luo, L., Gu, Y., & Ren, F. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3527010 · Cited by 8
“We assess the effectiveness of our proposed CoA on five in-the-wild FER datasets: RAF-DB [8], FER-Plus [9], AffectNet [10], SFEW [60], and CAER-S [61].”
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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