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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]
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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].”
MethodInfluentialRegion Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
Wang, K., Peng, X., Yang, J., Meng, D., & Qiao, Y. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2956143 · Cited by 970
Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism
Li, Y., Zeng, J., Shan, S., & Chen, X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2886767 · Cited by 916
Suppressing Uncertainties for Large-Scale Facial Expression Recognition
Wang, K., Peng, X., Yang, J., Lu, S., & Qiao, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00693 · Cited by 772
“…have made significant progress on facial expression recognition (FER) with algorithms and large-scale datasets, where datasets can be collected in lab-∗ oratory or in the wild, such as CK+ [29], MMI [39], Oulu-CASIA [47], SFEW/AFEW [10], FERPlus [4], AffectNet [32], EmotioNet [11], RAF-DB [22], etc.”
BackgroundLopes, A. T., de Aguiar, E., De Souza, A. F., & Oliveira-Santos, T. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.07.026 · Cited by 799
“Experiments were performed in the CK+ [4], MMI [54] and SFEW [55] datasets.”
MethodInfluentialCollecting Large, Richly Annotated Facial-Expression Databases from Movies
Dhall, A., Goecke, R., Lucey, S., & Gedeon, T. (2012) · IEEE Multimedia · doi:10.1109/mmul.2012.26 · Cited by 658
“Inspired by the Labeled Faces in the Wild (LFW) database,(1) we call our temporal database Acted Facial Expressions in the Wild (AFEW) and its static subset Static Facial Expressions in the Wild (SFEW).(2) In this context, ‘‘in the wild’’ refers to the challenging conditions in which the facial expressions occur rathe…”
MethodInfluentialSelf-citationDeep Learning for Emotion Recognition on Small Datasets using Transfer Learning
Ng, H.-W., Nguyen, V. D., Vonikakis, V., & Winkler, S. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction · doi:10.1145/2818346.2830593 · Cited by 657
“Images are selected from movies, in a semi-automated way, via a system based on subtitles [5, 6].”
MethodA systematic review on affective computing: emotion models, databases, and recent advances
Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.03.009 · Cited by 708
Image based Static Facial Expression Recognition with Multiple Deep Network Learning
Yu, Z., & Zhang, C. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2830595 · Cited by 602
“Categories and Subject Descriptors I.5.4 [Pattern Recognition]: Applications—computer vision, signal processing ; I.4.m [Image Processing and Computer Vision]: Miscellaneous Keywords Facial Expression Recognition; Convolutional Neural Network; Multiple Network Learning; EmotiW 2015 Challenge”
BackgroundInfluentialCorneanu, 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
“While previous RGB datasets record FEs in controlled lab environments, Acted Facial Expressions In The Wild Database (AFEW) [203], Affectiva-MIT Facial Expression Dataset (AMFED) [204] and SEMAINE [205] contain faces in naturalistic environments.”
MethodInfluentialKhare, S. K., Blanes‐Vidal, V., Nadimi, E. S., & Acharya, U. R. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102019 · Cited by 508
Facial Expression Recognition with Inconsistently Annotated Datasets
Zeng, J., Shan, S., & Chen, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_14 · Cited by 354
“Datasets have sprung up for both in-the-lab and in-the-wild facial expressions, such as CK+[20], MMI[28], Oulu-CASIA[33], SFEW/AFEW[7], AffectNet[22], EmotioNet[2], RAF-DB[16], and others.”
BackgroundInfluentialA survey on facial emotion recognition techniques: A state-of-the-art literature review
Canal, F. Z., Müller, T. R., Matias, J. C., Scotton, G. G., de Sá, A. R., Pozzebon, E., & Sobieranski, A. C. (2021) · Information Sciences · doi:10.1016/j.ins.2021.10.005 · Cited by 358
Facial emotion recognition using deep learning: review and insights
Mellouk, W., & Handouzi, W. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.07.101 · Cited by 370
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns
Levi, G., & Hassner, T. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction · doi:10.1145/2818346.2830587 · Cited by 336
“More recently, unconstrained, in-the-wild photos have been considered with the release of the EmotiW challenge [9].”
BackgroundDistract Your Attention: Multi-Head Cross Attention Network for Facial Expression Recognition
Wen, Z., Lin, W., Wang, T., & Xu, G. (2023) · Biomimetics · doi:10.3390/biomimetics8020199 · Cited by 363
“0 [44] is the newest version for SFEW dataset in which each facial emotion is extracted from the static frames of the AFEW video database.”
BackgroundFacial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks
Hasani, B., & Mahoor, M. H. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.282 · Cited by 291
“Since our method is designed mainly for classifying sequences of inputs, databases that contain only independent unrelated still images of facial expressions such as MultiPie [18] , SFEW [11] , FER2013 [17] cannot be examined by our method.”
MethodShe, J., Hu, Y., Shi, H., Wang, J., Shen, Q., & Mei, T. (2021) · 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/cvpr46437.2021.00618 · Cited by 298
“SFEW [12] contains the images from movies with seven basic emotions, including 958 images for training and 436 images for testing.”
BackgroundJoint Pose and Expression Modeling for Facial Expression Recognition
Zhang, F., Zhang, T., Mao, Q., & Xu, C. (2018) · 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/cvpr.2018.00354 · Cited by 270
“The Static Facial Expressions in the wild (SFEW) dataset [7] contains only 700 images (including both training and testing) while the Multi-PIE [13] has 7,655 images (5 poses and 6 expressions).”
BackgroundMethodInfluentialContext-Aware Emotion Recognition Networks
Lee, J., Kim, S., Kim, S., Park, J., & Sohn, K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01024 · Cited by 323
“Recently, datasets recorded in the wild condition for including naturalistic emotion states [9, 25, 26] have attracted much attention.”
BackgroundDeep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243
Label Distribution Learning on Auxiliary Label Space Graphs for Facial Expression Recognition
Chen, S., Wang, J., Chen, Y., Shi, Z., Geng, X., & Rui, Y. (2020) · 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/cvpr42600.2020.01400 · Cited by 239
“We mainly used two types of datasets, including in-the-wild datasets (RAF [22], AffectNet [28] and SFEW [8]) and lab-controlled datasets (CK+ [27], CFEE [9], MMI [34] and Oulu-CASIA [42]).”
BackgroundMethodAU-aware Deep Networks for facial expression recognition
Liu, M., Li, S., Shan, S., & Chen, X. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553734 · Cited by 229
AU-inspired Deep Networks for Facial Expression Feature Learning
Liu, M., Li, S., Shan, S., & Chen, X. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.02.011 · Cited by 223
From individual to group-level emotion recognition: EmotiW 5.0
Dhall, A., Goecke, R., Ghosh, S., Joshi, J., Hoey, J., & Gedeon, T. (2017) · Proceedings of the 19th ACM International Conference on Multimodal Interaction · doi:10.1145/3136755.3143004 · Cited by 218
“A total of 22 teams participated in the new sub-challenge and the Static Facial Expressions in the Wild (SFEW) 2.0 database [5] was used.”
BackgroundInfluentialSelf-citationFeature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition
Ruan, D., Yan, Y., Lai, S., Chai, Z., Shen, C., & Wang, H. (2021) · 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/cvpr46437.2021.00757 · Cited by 233
“In particular, FDRL achieves 89.47% and 62.16% recognition accuracy on the RAF-DB and SFEW databases, respectively.”
InfluentialEmotion Recognition In The Wild Challenge 2014
Dhall, A., Goecke, R., Joshi, J., Sikka, K., & Gedeon, T. (2014) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2663204.2666275 · Cited by 216
“Facial Expression Recognition in the Wild: The Influence of Temporal Information.”
BackgroundInfluentialSelf-citationMultimodal 2D+3D Facial Expression Recognition With Deep Fusion Convolutional Neural Network
Li, H., Sun, J., Xu, Z., & Chen, L. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2713408 · Cited by 211
“, Acted Facial Expressions in the Wild (AFEW) [10] and Static Facial Expressions in the Wild (SFEW) [9]), leaning facial representations using deep learning is becoming the mainstream in”
BackgroundXie, S., Hu, H., & Wu, Y. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.03.019 · Cited by 209
A Review on Automatic Facial Expression Recognition Systems Assisted by Multimodal Sensor Data
Samadiani, N., Huang, G., Cai, B., Luo, W., Chi, C., Xiang, Y., & He, J. (2019) · Sensors · doi:10.3390/s19081863 · Cited by 219
“It supplies images which contained a variety of challenges of facial expression recognition such as head poses, and illumination variation, broad age range, various faces existed in the real world [105].”
BackgroundEleftheriadis, S., Rudovic, O. O., & Pantić, M. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2375634 · Cited by 211
“Finally, for the experiments on SFEW we adopted the configuration proposed by the creators of the dataset in [48].”
BackgroundMethodInfluentialEmotion Classification with Data Augmentation Using Generative Adversarial Networks
Zhu, X., Liu, Y., Li, J., Wan, T., & Qin, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93040-4_28 · Cited by 203
“In our experiments, three benchmark datasets are tested: Facial Expression Recognition Database (FER2013) [10], Static Facial Expressions in the Wild (SFEW) [5] and The Japanese Female Facial Expression (JAFFE) Database [18].”
MethodDriver’s Facial Expression Recognition in Real-Time for Safe Driving
Jeong, M., & Ko, B. C. (2018) · Sensors · doi:10.3390/s18124270 · Cited by 198
“We improve the classification performance by changing the hierarchical structure of the classifier and improve the WRF instead of using a general RF.”
MethodFacial Expression Recognition: A Survey
Huang, Y., Chen, F., Lv, S., & Wang, X. (2019) · Symmetry · doi:10.3390/sym11101189 · Cited by 195
“Static Facial Expressions in the Wild Dataset (SFEW) [117] is developed by selecting frames from AFEW, has been divided into three sets: Train (958 samples), Val (436 samples) and Test (372 samples).”
BackgroundMethodEmotion recognition from unimodal to multimodal analysis: A review
Ezzameli, K., & Mahersia, H. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101847 · Cited by 206
Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality
Mehta, D., Siddiqui, M. F. H., & Javaid, A. Y. (2018) · Sensors · doi:10.3390/s18020416 · Cited by 197
“The experiments were done using Static Facial Expressions in the Wild (SFEW) database [38], SVM was trained and tested on the output responses of the concatenated network.”
MethodKarnati, M., Seal, A., Bhattacharjee, D., Yazidi, A., & Krejcar, O. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3243661 · Cited by 182
“…face database (RAFDB) [78], AffectNet [79], Oulu-CASIA [73], radboud faces database (RaFD) [69], static facial expressions in the wild (SFEW) [72], database of facial expressions (DaFEx) [64], multi-PIE [68], acted facial expressions in the wild (AFEW) [74], expression in the wild database…”
BackgroundVision Transformer With Attentive Pooling for Robust Facial Expression Recognition
Xue, F., Wang, Q., Tan, Z., Ma, Z., & Guo, G. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3226473 · Cited by 183
“We evaluate our proposed method on six in-the-wild FER datasets: RAF-DB [4], FERPlus [49], AffectNet [3], SFEW [50], ExpW [51], and Aff-Wild2 [52].”
MethodFacial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges
Patel, K., Mehta, D., Mistry, C., Gupta, R., Tanwar, S., Kumar, N., & Alazab, M. (2020) · IEEE Access · doi:10.1109/access.2020.2993803 · Cited by 157
“IT was designed by selecting frames (static) from the acted facial expressions in wild (AFEW) [67].”
MethodImpact of Deep Learning Approaches on Facial Expression Recognition in Healthcare Industries
Bisogni, C., Castiglione, A., Hossain, S., Narducci, F., & Umer, S. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3141400 · Cited by 160
Emotion recognition in the wild challenge 2013
Dhall, A., Goecke, R., Joshi, J., Wagner, M., & Gedeon, T. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531739 · Cited by 178
“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.”
BackgroundSelf-citationEmotiW 2016: video and group-level emotion recognition challenges
Dhall, A., Goecke, R., Joshi, J., Hoey, J., & Gedeon, T. (2016) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2993148.2997638 · Cited by 148
“A total of 22 teams participated in the new sub-challenge and the Static Facial Ex-pressions in the Wild (SFEW) 2.0 database [8] was used as the benchmarking data.”
MethodInfluentialSelf-citationA Deeper Look at Facial Expression Dataset Bias
Li, S., & Deng, W. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2973158 · Cited by 149
“(8) SFEW 2.0 Database [3]: The in-the-wild database SFEW contains dynamic images selected from different films with spontaneous expressions, various head pose, age range, occlusions and illuminations.”
BackgroundInfluentialChen, T., Pu, T., Wu, H., Xie, Y., Liu, L., & Lin, L. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3131222 · Cited by 157
“0 [11] is an in-the-wild dataset collected from different films with spontaneous expressions, various head poses, age ranges, occlusions and illuminations.”
BackgroundAutomatic Recognition of Student Engagement Using Deep Learning and Facial Expression
Nezami, O. M., Dras, M., Hamey, L., Richards, D., Wan, S., & Paris, C. L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46133-1_17 · Cited by 138
“zami et al. images including human faces. Yu et al. [37] employed a CNN model that was pretrained on the FER-2013 dataset [12] and fine-tuned on the Static Facial Expression in the Wild (SFEW) dataset [8]. They applied a face detection method to detect faces and remove noise in their target data samples. Mollahosseini…”
MethodKalateh, S., Estrada-Jimenez, L. A., Nikghadam-Hojjati, S., & Barata, J. (2024) · IEEE Access · doi:10.1109/access.2024.3430850 · Cited by 170
Advances in Facial Expression Recognition: A Survey of Methods, Benchmarks, Models, and Datasets
Kopalidis, T., Solachidis, V., Vretos, N., & Daras, P. (2024) · Information · doi:10.3390/info15030135 · Cited by 205
Geometry Guided Pose-Invariant Facial Expression Recognition
Zhang, F., Zhang, T., Mao, Q., & Xu, C. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2972114 · Cited by 135
“As shown in Table I, for example, the Static Facial Expressions in the wild (SFEW) dataset [25] contains only 700 images (including both training and testing) while the Multi-PIE [26] has 7,655 images (5 poses and 6 expressions).”
BackgroundMethodInfluentialFacial Emotion Expressions in Human–Robot Interaction: A Survey
Rawal, N., & Stock‐Homburg, R. (2022) · International Journal of Social Robotics · doi:10.1007/s12369-022-00867-0 · Cited by 152
“Zhang et al. [146] used a deep convolutional network (DCN) that had an accuracy of 98.9% on CK+ dataset and 55.27% on Static Facial Expressions in the Wild (SFEW) dataset.”
BackgroundInfluentialDAiSEE: Towards User Engagement Recognition in the WildPreprint
Gupta, A., D'Cunha, A., Awasthi, K., & Balasubramanian, V. N. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.01885 · Cited by 250
“A static subset (SFEW) [23] of 700 images and 95 subjects was created by selecting some frames from AFEW and it contains unconstrained facial expressions, different head poses and close to real-world scenarios like occlusions and unconstrained illumination.”
BackgroundTowards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin
Li, H., Wang, N., Yang, X., Wang, X., & Gao, X. (2022) · 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/cvpr52688.2022.00413 · Cited by 118
“SFEW [6] is a static facial expression dataset selected from movies, including 958 images for training, 436 images for validation and 372 images for testing.”
InfluentialHu, M. Q., Wang, H., Wang, X., Yang, J., & Wang, R. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.12.039 · Cited by 117
Gera, D., & Balasubramanian, S. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.01.029 · Cited by 119
“SFEW [12] contains selected frames from AFEW [11].”
InfluentialKim, B.-K., Dong, S.-Y., Roh, J., Kim, G.-M., & Lee, S.-Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.187 · Cited by 100
“However, many studies have applied some “manual” steps when this preprocessing fails.”
BackgroundA visual attention based ROI detection method for facial expression recognition
Sun, W., Zhao, H., & Jin, Z. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.03.034 · Cited by 110
Emotion Recognition in the Wild with Feature Fusion and Multiple Kernel Learning
Chen, J., Chen, Z., Chi, Z., & Fu, H. C. (2014) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2663204.2666277 · Cited by 104
“Although movies are shot in more or less controlled environments, they are close to real world environments, and the expressions are more realistic than the lab-controlled expressions [5].”
BackgroundCF-DAN: Facial-expression recognition based on cross-fusion dual-attention network
Zhang, F., Chen, G., Wang, H., & Zhang, C. (2024) · Computational Visual Media · doi:10.1007/s41095-023-0369-x · Cited by 109
“The effectiveness of the proposed model is compared with commonly used methods of recent years.”
Facial emotion recognition using convolutional neural networks
Sarvakar, K., Senkamalavalli, R., Raghavendra, S., Kumar, J. S., Manjunath, R., & Jaiswal, S. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.07.297 · Cited by 116
DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients
Sharma, S., Dudeja, R. K., Aujla, G. S., Bali, R. S., & Kumar, N. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05327-2 · Cited by 96
“Figure 8 depicts the comparative representation of classification accuracy for these three models for FER and SFEW datasets.”
Fast facial expression recognition using local binary features and shallow neural networks
Gogić, I., Manhart, M., Pandzic, I. S., & Ahlberg, J. (2018) · The Visual Computer · doi:10.1007/s00371-018-1585-8 · Cited by 98
“In the first stage, they used the large FER 2013 data set and finally the SFEW 2.0 training set.”
BackgroundInfluentialHistogram-based local descriptors for facial expression recognition (FER): A comprehensive study
Turan, C., & Lam, K. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.05.024 · Cited by 92
Discriminative deep multi-task learning for facial expression recognition
Zheng, H., Wang, R., Ji, W., Zong, M., Wong, W. K., Lai, Z., & Lv, H. (2020) · Information Sciences · doi:10.1016/j.ins.2020.04.041 · Cited by 91
“We implement DDMTL on three datasets: (i) the extended Cohn-Kanade dataset (CK + ) [25], (ii) the MMI facial expression dataset (MMI) [41], and (iii) the Static Facial Expressions in the Wild dataset (SFEW) [14].”
MethodIdentity- and Pose-Robust Facial Expression Recognition through Adversarial Feature Learning
Wang, C., Wang, S., & Liang, G. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350872 · Cited by 88
“We conduct experiments on five publicly available facial expression databases: Multi-PIE [6], BU-3DFE [22], SFEW [2], AffectNet [15] and FER2013 [5].”
MethodJi, Y., Hu, Y., Yang, Y., Shen, F., & Shen, H. T. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.12.037 · Cited by 86
EMOTIC: Emotions in Context Dataset
Kosti, R., Álvarez, J. M., Recasens, A., & Lapedriza, À. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.285 · Cited by 91
“1) The AFEW database [6] focuses on emotion recognition from video frames taken from movies and TV shows; where the actions are semi-spontaneous and are annotated with attributes like name, age of actor, age of character, pose, gender, expression of person, the overall clip expression and the basic 6 emotions and a ne…”
MethodFacial Expression Recognition from World Wild Web
Mollahosseini, A., Hassani, B., Salvador, M. J., Abdollahi, H., Chan, D., & Mahoor, M. H. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.188 · Cited by 83
“Existing databases in the wild settings, such as SFEW [3] or ar X iv :1 60 5.”
BackgroundInfluentialAutomatic Emotion Recognition for Groups: A Review
Veltmeijer, E. A., Gerritsen, C., & Hindriks, K. V. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3065726 · Cited by 87
“2,622,000 - GENKI-4K [46], [47] 4000 Smiling/non-smiling RAF-DB [48] 29,672 Basic and compound emotions FER+ [49] 36,887 Basic emotions SFEW [50] 700 Basic emotions”
Uncertainty-aware Label Distribution Learning for Facial Expression Recognition
Le, N., Nguyen, K., Tran, Q. D., Tjiputra, E., Le, B. H., & Nguyen, A. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00603 · Cited by 89
“We further perform experiments on the original AffectNet, RAF-DB, and SFEW to evaluate the robustness of our method to the uncertainty and ambiguity that unavoidably exists in real-world FER datasets.”
BackgroundMethodInfluentialFine-Grained Facial Expression Recognition in the Wild
Liang, L., Lang, C., Li, Y., Feng, S., & Zhao, J. C. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3007327 · Cited by 82
“However, the existing benchmark datasets [5]–[9], [34], [51] are not suitable for such a new task.”
BackgroundDeep Disturbance-Disentangled Learning for Facial Expression Recognition
Ruan, D., Yan, Y., Chen, S., Xue, J., & Wang, H. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413907 · Cited by 77
“20]) and (b) high intra-class difference (the images are from the SFEW database [5]).”
BackgroundDapogny, A., Bailly, K., & Dubuisson, S. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1010-1 · Cited by 79
Does “lie to me” lie to you? An evaluation of facial clues to high-stakes deception
Su, L., & Levine, M. D. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.01.009 · Cited by 79
“22 SFEW [83] Unconstrained Unconstrained Unconstrained Unconstrained AFEW [84] Unconstrained Unconstrained Unconstrained Unconstrained SMIC [85] Constant Constant Glasses Frontal pose USF-HD [86] Constant Constant Unknown Frontal pose CASME [87] Constant Constant Glasses Frontal pose”
BackgroundHierarchical Bayesian Theme Models for Multipose Facial Expression Recognition
Mao, Q.-R., Rao, Q.-Y., Yu, Y.-B., & Dong, M. (2017) · IEEE transactions on multimedia · doi:10.1109/tmm.2016.2629282 · Cited by 65
“So, the testing set contains 327 images in SFEW with these emotion labels.”
BackgroundMethodInfluentialHossain, S., Umer, S., Rout, R. K., & Tanveer, M. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.109997 · Cited by 77
Self-supervised vision transformer-based few-shot learning for facial expression recognition
Chen, X., Zheng, X., Sun, K., Liu, W., & Zhang, Y. (2023) · Information Sciences · doi:10.1016/j.ins.2023.03.105 · Cited by 81
Facial expression recognition in the wild based on multimodal texture features
Sun, B., Li, L., Zhou, G., & He, J. (2016) · Journal of Electronic Imaging · doi:10.1117/1.jei.25.6.061407 · Cited by 74
MEC 2016: The Multimodal Emotion Recognition Challenge of CCPR 2016
Li, Y., Tao, J., Schuller, B. W., Shan, S., Jiang, D., & Jia, J. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-3005-5_55 · Cited by 73
“Acted Facial Expressions in the Wild (AFEW) [2] and Static Facial Expressions in the Wild (SFEW) [3] are recent emotion databases representing (closer to) real-world scenarios.”
BackgroundOccluded Facial Expression Recognition Enhanced through Privileged Information
Pan, B., Wang, S., & Xia, B. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3351049 · Cited by 70
“Follow the experimental condition in [8], both within-database and cross-database experiments are conducted on seven benchmark databases including RAF-DB [6], AffectNet [10], CK+ [9], MMI [11], Oulu-CASIA [18], SFEW [2] and FED-RO.”
MethodS., A. T., & Guddeti, R. R. (2020) · Future generations computer systems · doi:10.1016/j.future.2020.02.075 · Cited by 72
Recognition of facial expressions based on CNN features
González-Lozoya, S. M., de la Calleja, J., Pellegrin, L., Escalante, H. J., Nieto, M. A. M., & Benitez-Ruiz, A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08681-4 · Cited by 73
“Although there are some efforts to build databases recreating in the wild settings such as [3, 4, 8], some Web images have low resolution and they do not have facial landmark points that are necessary for preprocessing, the released facial location and landmarks do not capture the faces in all images correctly, making…”
BackgroundFederated Multidomain Learning With Graph Ensemble Autoencoder GMM for Emotion Recognition
Zhang, C., Li, M., & Wu, D. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3203800 · Cited by 71
“A. Dataset SFEW [43]: Based on face point clustering key frame calculation, static frames are selected from the AFEW database to generate static face expressions.”
MethodNew Trends in Emotion Recognition Using Image Analysis by Neural Networks, a Systematic Review
Cîrneanu, A.-L., Popescu, D., & Iordache, D. D. (2023) · Sensors · doi:10.3390/s23167092 · Cited by 84
“SFEW [67] in-the-wild 1739 images color 330 anger, disgust, fear, neutral, happiness, sadness, surprise [68,69]”
LSTM for dynamic emotion and group emotion recognition in the wild
Sun, B., Wei, Q., Li, L., Xu, Q., He, J. Y. D., & Yu, L. (2016) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2993148.2997640 · Cited by 71
Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition
Xie, Y., Chen, T., Pu, T., Wu, H., & Lin, L. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413822 · Cited by 65
“en using the same ResNet-50 as the backbone and the same RAF-DB [23] as the source dataset, the proposed framework improves the accuracy averaging over the CK+ [30], JAFFE [31], FER2013 [14], SFEW2.0 [9], and ExpW [55] datasets by 4.02% compared with the previous best-performing works. The codes and trained models are…”
BackgroundMethodInfluentialEmotion Recognition on FER-2013 Face Images Using Fine-Tuned VGG-16
Kusuma, G. P., Jonathan, J., & Lim, A. P. (2020) · Advances in Science Technology and Engineering Systems Journal · doi:10.25046/aj050638 · Cited by 74
“The reviews from [15–19] described many available facial emotion recognition datasets, such as Facial Emotion Recognition 2013 (FER-2013) [1, 9], Static Facial Emotion in the Wild (SFEW) [10], Japanese Association of Female Facial Expression (JAFFE) [11], Cohn-Kanade (CK) [12], Extended Cohn-Kanade (CK+) [13], and Exp…”
BackgroundDeep Emotion Transfer Network for Cross-database Facial Expression Recognition
Li, S., & Deng, W. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545284 · Cited by 66
“During experiments, the real-world facial expression database RAF-DB [10] was used as the source domain, and extensive evaluations have been conducted on different well-established target databases in the literature: CK+ [1], JAFFE [11], MMI [12], SFEW [13] and FER2013 [14].”
BackgroundMethodInfluentialMetrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognition
Dominguez-Catena, I., Paternain, D., & Galar, M. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3361979 · Cited by 82
“org/paper/The-MUG-facial-expression-database-Aifanti-Papachristou/ f1af714b92372c8e606485a3982eab2f16772ad8 [71] A.”
Afzal, S., Khan, H. A., Piran, M. J., & Lee, J. W. (2024) · IEEE Access · doi:10.1109/access.2024.3422480 · Cited by 86
“Oulu [98] 80 SBE FER2013 [99] 32 SBE SFEW [100] 32 SBE EmotionNEt [93] 32 Compound Oulu [98] 32 SBE ExpW [94] 32 SBE+ AffectNEt [95] 32 SBE+ CASME II [101] 35s SBE + SMIC [102] 16 3, positive, negatice, surprice 4DFAB [103] 180 SBE 3DFE [92] 100 SBE MMI [91] 25 SBE CK+ [90] 123 SBE+ CK [104] 210 SBE+ JAFFE [105] 10 SBE”
Learning to Amend Facial Expression Representation via De-albino and AffinityPreprint
Shi, J., Zhu, S., & Liang, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.10189 · Cited by 82
Sentiment Analysis: Comprehensive Reviews, Recent Advances, and Open Challenges
Lu, Q., Sun, X., Long, Y., Gao, Z., Feng, J. H., & Sun, T. S. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3294810 · Cited by 77
Contrasting and Combining Least Squares Based Learners for Emotion Recognition in the Wild
Kaya, H., Gürpınar, F., Afshar, S., & Salah, A. A. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2830588 · Cited by 63
Meguid, M. K. A. E., & Levine, M. D. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2317711 · Cited by 62
Convolutional relation network for facial expression recognition in the wild with few-shot learning
Zhu, Q., Mao, Q., Jia, H., Noi, O. E. N., & Tu, J. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116046 · Cited by 61
Novel deep learning model for facial expression recognition based on maximum boosted CNN and LSTM
Rajan, S., Chenniappan, P., Devaraj, S., & Madian, N. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.1188 · Cited by 64
Weakly-Supervised Facial Expression Recognition in the Wild With Noisy Data
Zhang, F., Xu, M., & Xu, C. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3072786 · Cited by 59
Survey on real‐time facial expression recognition techniques
Deshmukh, S., Patwardhan, M. S., & Mahajan, A. (2016) · IET Biometrics · doi:10.1049/iet-bmt.2014.0104 · Cited by 60
“On the other hand, SFEW is a static dataset, which captures facial expressions in tough conditions such as lighting conditions, resolution etc. [35].”
BackgroundMethod
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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