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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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Items 401–500 of 541
Cross-domain facial expression recognition: Bi-Directional Fusion of Active and Stable Information
Zhu, Y., Ai, J., Xue, W., Wu, M., Yang, S., Jia, W., & Hu, M. Q. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110357 · Cited by 3
Cross-Cultural Facial Expression Recognition: A Deep Learning Study On Indian Cultural Diversity
Singh, S., Yadav, K. S., Gupta, P., et al. (2025) · National Conference on Communications · doi:10.1109/ncc63735.2025.10982913 · Cited by 3
Fusion of Hand-crafted and Deep Features for Empathy Prediction
Hinduja, S., Uddin, M. T., Jannat, S. R., et al. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756522 · Cited by 3
“0 [5] datasets that contain audio and image data, to mimic”
BackgroundSupervised Cross-Database Transfer Learning-Based Facial Expression Recognition
Gupta, A., & Balakrishnan, R. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-33-6691-6_30 · Cited by 3
Deep Neural Networks for Automatic Facial Expression Recognition
Veesam, V. S., Ravichandran, S., & Babu, G. R. M. (2022) · Revue d intelligence artificielle · doi:10.18280/ria.360520 · Cited by 3
“Anger, Disgust, Neutral, Happy, Squint, Scream, Surprise SFEW [33] 700 photos with varying ages, occlusion, lighting, and head poses.”
3D convolution network and Siamese-attention mechanism for expression recognition
Zhang, Y., Xia, T., & Liu, Y. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07860-2 · Cited by 3
“9% Mollahosseini [25] MMI, MultiPIE [10], DISFA, FERA, SFEW [7], FER2013 [9] CK+ 62.”
Attention Mechanism and Feature Correction Fusion Model for Facial Expression Recognition
Xu, Q., Wang, C., & Hou, Y. (2021) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict50816.2021.9358800 · Cited by 4
“In the past decades, many expression databases have been constructed in lab-controlled or in the wild, such as MMI [19], CK+ [20], Oulu-CASIA [21], Multi-PIE [22], SEFW/AEFW [23], FER2013 [10], ExpW [24], EmotionNet [25], RAF-DB [15], AffectNet [26], DFEW [27].”
BackgroundStudy of robust facial recognition under occlusion using different techniques
Jayant, D., & Bhat, A. (2021) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs51141.2021.9432300 · Cited by 3
Sreenivas, M., Takamuku, S., Biswas, S., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25072-9_17 · Cited by 3
Lossless Attention in Convolutional Networks for Facial Expression Recognition in the WildPreprint
Wang, C., Hu, R., Hu, M., Liu, J., Ren, T., He, S., Jiang, M. X., & Miao, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.11869 · Cited by 3
Context-aware emotion recognition through agent-scene interactions
Chen, Y.-X., Sun, H., Che, C.-Y., Feng, S., & Jia, R. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111581 · Cited by 2
Multimodal Representation Learning Techniques for Comprehensive Facial State Analysis
Zheng, K., Ge, X., Fu, J., et al. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11208908 · Cited by 2
Liang, Y., Cui, J., Gao, R., Chen, C., Chen, F., Mo, L., & Pan, J. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3646217 · Cited by 2
“We compared our method with existing state-of-the-art methods across two categories of datasets: DFER datasets (DFEW [3], AFEW [31], FERV39k [14], MAFW [15], and HM-Class) and SFER datasets (RAF-DB [37] and SFEW [36]).”
LBP and SIFT based facial expression recognition
Sümer, Ö., & Güneş, E. O. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2181505 · Cited by 2
“Before our results on SFEW database, let's review the definition of these metrics which are defined as a part of SPI BEFIT challenge in [6] briefly.”
MethodResultInfluentialVisual Methods for Sign Language Recognition: A Modality-Based Review
Seddik, B., & Amara, N. E. B. (2021) · Smart sensors, measurement and instrumentation · doi:10.1007/978-3-030-81982-8_9 · Cited by 2
“ssions and additional 19 illuminations [99]. The Static Facial Expressions in the Wild (SFEW) dataset, extracted from its dynamic version AFEW, was the rst to oer challenging in-the-wild expressions [100]. The training population continued to grow in the FER-2013 [101] and ExpW [90] datasets, to reach 36K then 92K fac…”
BackgroundMethodInfluentialVirtual Assistant Based on Facial Emotions Using Convolution Neural Networks
Kumar, J. N. V. R. S., Indira, D. N. V. S. L. S., Abinaya, R., et al. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-8512-5_30 · Cited by 2
Class Imbalance in Facial Expression Recognition by GCN with Focal Loss
Wang, S., Tang, H., & Chai, L. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9727624 · Cited by 2
“According to different scenarios, these datasets can be divided into laboratory datasets (e.g., CK+ [2], JAFFE [3], MMI [4], Oulu-CASIA [5]) and non-laboratory datasets (e.g., RAF [6], SFEW 2.0 [7], ExpW [8], FER2013 [9]).”
BackgroundInfluentialLightweight Multi-level Information Fusion Network for Facial Expression Recognition
Zhang, Y., Tian, X., Zhang, Z., & Xu, X. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-27818-1_13 · Cited by 2
Facial Expression Synthesis and Recognition with Pre-Trained StyleGAN
Zhai, X., Ma, X., Tian, H., & Li, Y. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10240584 · Cited by 2
POSER: POsed vs Spontaneous Emotion Recognition using fractal encoding
Bisogni, C., Cascone, L., Nappi, M., & Pero, C. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.104952 · Cited by 2
FER20E: An Extended Facial Expression Recognition Dataset With 20 Discrete Emotions
Yadav, K. S., & Kumar, L. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3721357 · Cited by 2
Human Affect Recognition Using Deep Learning Approaches
Seshaiah, M., Bhargavi, D., Hema, H. P., Mallikashree, N., & Manuja, C. R. (2024) · International Conference on Knowledge Engineering and Communication Systems (ICKECS) · doi:10.1109/ickecs61492.2024.10616539 · Cited by 2
A Survey on Graph Deep Representation Learning for Facial Expression Recognition
Gueuret, T., Sellami, A., & Djéraba, C. (2024) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi62980.2024.10858877 · Cited by 2
“It then uses the correlation of different types of features, for example, holistic and local, to create a single robust graph that is able to adapt to different domains.”
Exploring Semi-Supervised Learning in Facial Expression Recognition
Wang, Y., Chen, H., Xu, Q., et al. (2024) · doi:10.1109/isdh64927.2024.00034 · Cited by 2
“SFEW [24] consists of 958 training images and 436 validating images.”
Affective question answering on video
Ruwa, N., Mao, Q., Wang, L., & Gou, J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.06.046 · Cited by 2
Expression analysis in the wild
Dhall, A. (2013) · doi:10.1145/2461466.2461529 · Cited by 2
Self-citationShape and Appearance Based Sequenced Convnets to Detect Real-Time Face Attributes on Mobile Devices
Livet, N., & Berkowski, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-94544-6_8 · Cited by 2
Robust Facial Expression Recognition using Local Haar Mean Binary Pattern.
Goyani, M. M., & Patel, N. M. (2018) · Journal of information science and engineering · Cited by 2
Age and gender effects on the human’s ability to decode posed and naturalistic emotional faces
Esposito, A. A., Amorese, T., Cuciniello, M., Riviello, M. T., & Cordasco, G. (2022) · Pattern Analysis and Applications · doi:10.1007/s10044-021-01049-w · Cited by 2
“The static facial expressions in the wild (SFEW) database has been developed and validated by Dhall and colleagues [4] by selecting frames from the acted facial expressions in the wild (AFEW) database, which consists of dynamic emotional faces extracted from movies.”
BackgroundMethodRegion Dual Attention-Based Video Emotion Recognition
Liu, X., Xu, H., & Wang, M. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/6096325 · Cited by 2
“Similar to [32], we apply an 8:1:1 data set partition.”
MethodSabharwal, R., & Rizvi, S. W. A. (2022) · Proceedings in adaptation, learning and optimization · doi:10.1007/978-3-030-97196-0_6 · Cited by 2
Patch-Range Attention and Visual Transformer for Facial Expression Recognition
Lu, Z., & Tang, X. (2022) · International Conference on Electronic Information Engineering and Computer Technology (EIECT) · doi:10.1109/eiect58010.2022.00044 · Cited by 2
“Comparison with State-of-the-Art Models CK+[48], FERPlus[16], RAF-DB[22] and SFEW[31] are used for evaluation.”
BackgroundMethodA Multi-Head Convolution Network with Attention Consistency for Facial Expression Recognition
Liu, W., Sun, M., & Li, Y. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10240537 · Cited by 2
Scheme fuzzy approach to classify skin tonalities through geographic distribution
Hernandez-Matamoros, A., Fujita, H., Nakano-Miyatake, M., Perez-Meana, H. M., & Escamilla-Hernández, E. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01400-4 · Cited by 2
Declarative Residual Network for Robust Facial Expression Recognition
Cui, R., Plested, J., & Liu, J. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-63820-7_39 · Cited by 2
Montenegro, J. M. F. (2018) · Research Repository (Kingston University London) · doi:10.48550/arxiv.1810.10941 · Cited by 3
“CK+ 2.6.1, JAFFE 2.6.1 and SFEW2 [117] databases were used for experimental testing, whereas TFEID [118], YALE [119] and EURECOM [120] were reserved for generalization testing.”
MethodInfluentialEmotion Detection and its Effect on Decision-making
Shehu, H. A. (2023) · Open Access Victoria University of Wellington | Te Herenga Waka (Figshare) · doi:10.26686/wgtn.23530485 · Cited by 2
A Survey on Facial Expression Recognition: Modality, Methodologies, Challenges and Emerging TopicsPreprint
Shahid, A. R. (2023) · doi:10.36227/techrxiv.23531241.v1 · Cited by 2
Semantic Data Augmentation for Long-tailed Facial Expression RecognitionPreprint
Li, Z., Wang, Y., Guan, B., et al. (2024) · Qeios · doi:10.32388/tsb725 · Cited by 2
Generative Adversarial Networks in Human Emotion Synthesis: A Review
Hajarolasvadi, N., Ramírez, M. A., Beccaro, W., & Demirel, H. (2020) · IEEE Access · doi:10.1109/access.2020.3042328 · Cited by 37
“Dv1 Oulu-CASIA [74] 80 2,880 6⋆ Dv2 MULTI-PIE [75] 337 755,370 6† Dv3 CK+ [76] 123 593 6◇ Dv4 BU-3DFE [77] 100 2,500 6● Dv5 RaFD [78] 67 1,068 6◇ Dv6 CelebA [79] 10,177 202,599 45+ Dv7 EmotioNet [80] N/A 1,000,000 23‡ Dv8 AffectNet [81] N/A 450,000 6● Dv9 FER2013 [82] N/A 35,887 6● Dv10 SFEW [83] N/A 1,766 6● Dv11 JAF…”
Semantic Data Augmentation for Long-tailed Facial Expression Recognition
Li, Z., Wang, Y., Guan, B., et al. (2023) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs57501.2023.10150674 · Cited by 1
Multi-Head Attention Affinity Diversity Sharing Network for Facial Expression Recognition
Zheng, C., Liu, J., Zhao, W. D., Ge, Y., & Chen, W. (2024) · Electronics · doi:10.3390/electronics13224410 · Cited by 1
Li, Z., Deng, Z., Li, X., Ke, W.-D., Yan, S.-J., Zhang, J.-D., & Liu, C. (2025) · Sensors · doi:10.3390/s25175258 · Cited by 1
“Others have collected data from real-world environments, such as outdoor sports or fieldwork [9].”
Enhancing Facial Emotion Recognition Using Advanced Convolutional Neural Networks
Kalas, M. S., & Gore, S. R. (2025) · International Conference on Smart & Sustainable Technology (INCSST) · doi:10.1109/incsst64791.2025.11210351 · Cited by 1
A Novel Approach for Facial Emotion Recognition: Deep CNN Feature Extraction Combined with ML Models
Gahlaut, A., Rana, C., Parihar, A. S., et al. (2025) · International Conference on Sustainable Communication Networks and Application (ICSCNA) · doi:10.1109/icscn67106.2025.11308380 · Cited by 1
Guo, Z., Liu, Y., Wang, K., et al. (2026) · The Visual Computer · doi:10.1007/s00371-026-04424-y · Cited by 1
Enhancing dynamic facial expression recognition through cooperative spatio-temporal feature learning
Yu, C., & Liu, M. (2026) · The Visual Computer · doi:10.1007/s00371-026-04499-7 · Cited by 1
“CNN-based methods such as AlexNet [15], VGGNet [16], and ResNet [17] achieved significant success on benchmarks like SFEW [18] and AffectNet [19], demonstrating the capability of deep models to capture expression-related features from single frames.”
ContextFace: Generating Facial Expressions from Emotional Contexts
Kim, M., Kim, M., & Baek, S. J. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01059 · Cited by 1
“We use two datasets SFEW [9] and CAER-S [16] and TV shows and are suitable for studying contextual emotions.”
Mambaer: Mamba with knowledge-learning hierarchical attention for facial expression recognition
Pei, E., Wei, X., He, L., Chen, H., Hu, Z., Ning, H., An, X., & Li, X. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02388-6 · Cited by 1
Boosting Semi-Supervised Learning With Entropy-Guided Adaptive Reward Maximization
Tong, A., Shi, Z., Zhong, Z., et al. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3715098 · Cited by 1
Performance analysis of classifiers for facial expression recognition under constrained settings
Alphonse, A. S., & Dejey, D. (2016) · International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE) · doi:10.1109/icctide.2016.7725337 · Cited by 1
“Static Facial Expression Database (SFEW) is such a database [2] generated from the key frames of movies collected using semiautomatic method from internet.”
MethodFSE: a Powerful Feature Augmentation Technique for Classification Task
Liu, Y., Yang, Y., & Hossain, M. Z. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-92270-2_55 · Cited by 1
How far Generated Data Can Impact Neural Networks Performance?
Picha, S. G., Chanti, D. A., & Caplier, A. (2023) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0011629000003417 · Cited by 1
“While it is undeniable that generated data is a costless method that can help improve FER model accuracy, the exact gain would be determined by the test databases in applications.”
MethodMulti-level feature fusion capsule network with self-attention for facial expression recognition
Huang, Z., Yu, S., & Liang, J. (2023) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.2.023038 · Cited by 1
“Thus, uncontrolled datasets that capture natural face expressions, such as SFEW 8 and RAF-DB, 5 provide a considerable challenge to FER researchers.”
BackgroundAffect Recognition in Muscular Response Signals
Boeker, M., Jakobsen, P., Riegler, M. A., Stabell, L. A., Fasmer, O. B., Halvorsen, P., & Hammer, H. L. (2023) · IEEE Access · doi:10.1109/access.2023.3279720 · Cited by 1
“Especially in the field of machine learning within affect recognition, the model has been used to create classification problems [20], [23], [24], [25].”
MethodZhang, Z., Hui, J., Lu, G., et al. (2022) · International Conference on Signal Processing Systems (ICSPS) · doi:10.1109/icsps58776.2022.00075 · Cited by 1
“This scheme ensures person independent because all facial expressions of one person in the test set could not present in the training set, generally known as Strictly Person Independent (SPI) [27] and LeaveOne-Person-Out (LOPO) cross-validation.”
MethodWang, X., Lu, X., Wang, Y., Wang, H., & Zhang, Z. (2023) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc58118.2023.10270302 · Cited by 1
ACLM: Adaptive Compensatory Label Mining for Facial Expression Recognition
Liu, C., Wang, S., Shuai, H., & Liu, Q. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46314-3_3 · Cited by 1
FACIAL EMOTION RECOGNITION USING CONVOLUTIONAL NEURAL NETWORK BASED ON THE VISUAL GEOMETRY GROUP-19
Prabaswera, D. R., & Soeparno, H. (2023) · Jurnal TAM (Technology Acceptance Model) · doi:10.56327/jurnaltam.v14i1.1475 · Cited by 1
“Currently, facial expression recognition is a challenging and interesting task, as evidenced by numerous previous competitions by researchers [1– 8], a group of available datasets [1,9–13], and research on the subject.”
BackgroundEnhancing Online Learning with Automated Emotion Identification using Facial Expressions
Jagadeesh, M., Zubair Ali., L., Vishnu, S., Sajeev, N., & Nithish Kumar, N. (2023) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca58529.2023.10395433 · Cited by 1
Channel Selective Relation Network for Efficient Few-shot Facial Expression Recognition
Kim, C.-L., Lee, G.-E., Choi, Y.-J., et al. (2024) · International Conference on Consumer Electronics · doi:10.1109/icce59016.2024.10444505 · Cited by 1
“Weexperimented on four different facial datasets: RAF-DB [10], SFEW [11], AFEW [12], and FER2013 [13].”
BackgroundPoh, J. Q., See, J., Gayar, N. E., & Wong, L.-K. (2024) · International Workshop on Multimodal and Responsible Affective Computing (MRAC) · doi:10.1145/3689092.3689409 · Cited by 2
“Early datasets like Static Facial Expressions in the Wild (SFEW) [7] focused on static images of facial expressions in uncontrolled environments.”
Adaptive Threshold-Driven Semi-Supervised Facial Expression Recognition
Wang, Y., Zhu, A., Yuan, J., & Xu, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0122-6_27 · Cited by 1
A Review on Facial Expression Recognition Models, Datasets, and Future Direction of Research
Singh, R., & Prabha, C. (2024) · International Conference on Sustainable Communication Networks and Application (ICSCNA) · doi:10.1109/icscna63714.2024.10864269 · Cited by 2
Facial expression pair matching
Engin, D., Ekenel, H. K., & Bölümü, B. M. (2017) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2017.7960646 · Cited by 1
“Yüz ifadesi tanımada kullanılan bazı veri kümeleri: Extended Cohn-Kanade (CK+) [4], MultiPIE [5], MMI [6], FERA [7], SFEW [8]’dir.”
Interest Indices for Human Computer Interaction Applications based on Facial Image Sequences
Bhattacharya, S., Dasgupta, A., Sengupta, A., & Routray, A. (2019) · IEEE India Conference · doi:10.1109/indicon47234.2019.9029016 · Cited by 1
“The network has been trained on the Acted Facial Expressions In The Wild (AFEW) dataset [17].”
MethodData Augmentation Using GANs for 3D Applications
Maniadis, I., Solachidis, V., Vretos, N., & Daras, P. (2020) · Advances in multimedia and interactive technologies book series · doi:10.4018/978-1-5225-5294-9.ch011 · Cited by 1
Evaluation of the Off-the-Shelf CNNs for Facial Expression Recognition
Shirazi, M. S., & Bati, S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-98015-3_32 · Cited by 1
A Comprehensive Investigation on Emotional Detection in Deep Learning
Anand, M., & Babu, S. (2022) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit228111 · Cited by 2
Emotion recognition from facial images
Smirnov, N. V., & Chernyshov, A. S. (2022) · International Russian Automation Conference (RusAutoCon) · doi:10.1109/rusautocon54946.2022.9896304 · Cited by 1
“The authors [4] proposed a novel architecture named Amending Representation Module (ARM) and achieved accuracy values of 90.42% on RAF-DB dataset [5], 65.2% on Affect-Net dataset [6], and 58.71% on SFEW dataset [7] respectively.”
InfluentialFacial expression recognition based on improved residual network
Liu, J., Luo, X., & Huang, Y. (2022) · doi:10.1117/12.2653443 · Cited by 1
Yang, F., Xie, W., Zhong, T., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20233-9_30 · Cited by 1
“0 [4], FER2013 [5] and EXPW [6] are used as target datasets, while only the source domain dataset are used for the training.”
MethodTang, S., & Pan, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.09871 · Cited by 1
“[21] proposed a LSTM based approach and fined tuned the AlexNet model [22] by training on the Static Facial Expressions in the Wild (SFEW) dataset [23] and the HAPPEI dataset [24].”
MethodDeep Multi-Facial patches Aggregation Network for Expression Classification from Face ImagesPreprint
Djerghri, A., Hazourli, A. R., & Othmani, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.10305 · Cited by 1
“Several works have been proposed for facial emotions classification from static images [24, 7, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38] or from continuous video input [39, 40, 41].”
BackgroundFacial Expression Recognition in Unconstrained Environment
Khan, S. A. (2019) · Cited by 1
Wadhawan, R., & Gandhi, T. K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.11274 · Cited by 1
“We use three datasets to evaluate our proposed methodology, the Extended CohnKanade (CK+) [1], Japanese Female Facial Expression (JAFFE) [2], and Static Expressions in the Wild (SFEW) [3], [4].”
BackgroundMethodManelis, L., Barami, T., Ilan, M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.14.653948 · Cited by 1
“Since most algorithms were trained with images of adults performing specific facial expressions, which o�en include posed and exaggerated expressions [40,41) , their ability to accurately iden�fy facial expressions of children during naturalis�c interac� ons may not be as high as o�en claimed by…”
Facial expression recognition under harsh lighting using high dynamic range imaging.
Ige, E. O. (2026) · Warwick Research Archive Portal (University of Warwick) · doi:10.82444/warw.33741808
Expression recognition in the wild with transfer learning
Xia, T., Zhang, Y., Liu, Y., & Sun, Y. (2017) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2017.8170910
“The independent training of PI and MLBP on FER2013 obtain different results with the experiment [24] on SFEW [9], in which the ensemble of diverse networks nearly abandon”
BackgroundResultInfluentialMilošević, J., Peña, D., Forembsky, A., Moloney, D., & Malek, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00046
“More information about it can be found at [10].”
BackgroundDeep Neural Networks for Human Behavior Understanding
Singh, R. K., & Nigam, S. (2019) · Springer eBooks · doi:10.1007/978-3-030-15887-3_32
Facial Expression Recognition Using Directional Gradient Local Ternary Patterns
Nour, N., & Viriri, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33709-4_8
Beyond without Forgetting: Multi-Task Learning for Classification with Disjoint DatasetsPreprint
Hong, Y., Niu, L., Zhang, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.06746
Kim, B. (2020) · Journal of Defense Acquisition and Technology · doi:10.33530/jdaat.2020.2.1.33
“other hand, SFEW (static facial expression in wild) [3]”
BackgroundDeep audio and video emotion detection
Aminbeidokhti, M. (2020) · Espace ÉTS (ETS)
Multimodal Emotion Recognition Using Non-Inertial Loss Function
Orgil, J., Karungaru, S., Terada, K., et al. (2021) · Journal of Signal Processing · doi:10.2299/jsp.25.73
Facial Expression Recognition In The Wild Using Bidirectional Convolutional Neural Network
Liu, J. (2021) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic51459.2021.9415198
A Pilot Study on the Use of Changes in Facial Features to Assess Physical Workload in Real-Time
Zhang, Q., & Cavuoto, L. A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80713-9_37
Occlusion-Aware Facial Expression Recognition Based Region Re-weight Network
Zhang, X., Zhang, X., Zhou, J., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89370-5_16
Two-stream Gabor-AGraph Convolutional Networks for Facial Expression Recognition
Jiang, S., Xu, X., Xing, X., Wang, L., & Liu, F. (2021) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg52635.2021.9666935
Augmenting Images with a Mid-Processing Unit to Enhance Classification Accuracy
Johnson, G., Argyriou, V., & Politis, C. (2022) · International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) · doi:10.1109/csndsp54353.2022.9907917
Facial Expression Recognition based on a Dual Graph Convolutional Network
Lu, W., Tang, H., & Chai, L. (2021) · CIE International Conference on Radar (RADAR) · doi:10.1109/radar53847.2021.10028020
“In order to be more relevant to the scenario of application, more and more datasets in non-laboratory settings (e.g. MMI [7], SFEW [8], RAF-DB [9], etc.) have emerged, which are mostly selected from scenarios such as web and video.”
BackgroundAgrwal, S. L., Sharma, S. K., & Kant, V. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-28183-9_41
Innovations and Insights of Sequence-Based Emotion Detection in Human Face Through Deep Learning
Kant, K., & Shah, D. B. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-1909-3_33
Decoding Humans' and Virtual Agents' Emotional Expressions
Amorese, T., Cordasco, G., Cuciniello, M., Shevaleva, O., Marrone, S., Vogel, C. M., & Esposito, A. A. (2023) · doi:10.1002/9781119863663.ch20
Fusing Multi-scale Binary Convolution with Joint Attention Face Expression Recognition Algorithm
Qin, M., & Li, L. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-6886-2_34
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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