The DOI matches its Crossref record; 24 of the 25 references it cites were found in a registry or an index.
FERA 2015 - second Facial Expression Recognition and Analysis challenge
Abstract
Despite efforts towards evaluation standards in facial expression analysis (e.g. FERA 2011), there is a need for up-to-date standardised evaluation procedures, focusing in particular on current challenges in the field. One of the challenges that is actively being addressed is the automatic estimation of expression intensities. To continue to provide a standardisation platform and to help the field progress beyond its current limitations, the FG 2015 Facial Expression Recognition and Analysis challenge (FERA 2015) will challenge participants to estimate FACS Action Unit (AU) intensity as well as AU occurrence on a common benchmark dataset with reliable manual annotations. Evaluation will be done using a clear and well-defined protocol. In this paper we present the second such challenge in automatic recognition of facial expressions, to be held in conjunction with the 11 IEEE conference on Face and Gesture Recognition, May 2015, in Ljubljana, Slovenia. Three sub-challenges are defined: the detection of AU occurrence, the estimation of AU intensity for pre-segmented data, and fully automatic AU intensity estimation. In this work we outline the evaluation protocol, the data used, and the results of a baseline method for the three sub-challenges.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“As a consequence the Facial Expression and Recognition (FERA) challenge added a special section for intensity estimation [231], [232].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [232]
Citations
Cites
Intraclass correlations: Uses in assessing rater reliability.
Shrout, P. E., & Fleiss, J. L. (1979) · Psychological Bulletin · doi:10.1037/0033-2909.86.2.420 · Cited by 24,532
Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
Powers, D. M. W. (2020) · arXiv · doi:10.48550/arxiv.2010.16061 · Cited by 5,440
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
Supervised Descent Method and Its Applications to Face Alignment
Xiong, X., & la Torre, F. D. (2013) · 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.2013.75 · Cited by 1,926
Automatic facial expression analysis: a survey
Fasel, B., & Luettin, J. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(02)00052-3 · Cited by 1,810
Zhang, W., Shan, S., Gao, W., Chen, X., & Zhang, H. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.147 · Cited by 1,037
BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database
Zhang, X., Yin, L., Cohn, J. F., Canavan, S., Reale, M., Horowitz, A., Liu, P., & Girard, J. M. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.06.002 · Cited by 745
Self-citationMcKeown, G., Valstar, M., Cowie, R., Pantic, M., & Schroder, M. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.20 · Cited by 665
Self-citation(2017) · The SAGE Encyclopedia of Communication Research Methods · doi:10.4135/9781483381411.n178 · Cited by 559
Flagged first, then by citations
1 of the 25 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- mckeown, 2015, The underdetermined nature of laughter: the blurring of boundaries in social signals, unknown
- wu, 2012, Multi-layer architectures of facial action unit recognition, IEEE Trans Systems Man and Cybernetics Part B
Cited by
Sorted by citations
OpenFace: An open source facial behavior analysis toolkit
Baltrušaitis, T., Robinson, P., & Morency, L. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477553 · Cited by 1,794
A review of affective computing: From unimodal analysis to multimodal fusion
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644
Facial 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
Corneanu, 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
Self-citationValstar, M., Gratch, J. S., Schuller, B. W., Ringeval, F., Lalanne, D., Torres, M. T., Scherer, S. J., Stratou, G., Cowie, R., & Pantić, M. (2016) · International Workshop on Audio/Visual Emotion Challenge (AVEC) · doi:10.1145/2988257.2988258 · Cited by 540
Self-citationMultimodal Spontaneous Emotion Corpus for Human Behavior Analysis
Zhang, Z., Girard, J. M., Wu, Y., Zhang, X., Liu, P., Çiftçi, U. A., Canavan, S. J., Reale, M. J., Horowitz, A., Yang, H., et al. (2016) · 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.2016.374 · Cited by 438
Self-citationCross-dataset learning and person-specific normalisation for automatic Action Unit detection
Baltrušaitis, T., Mahmoud, M., & Robinson, P. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7284869 · Cited by 415
Automatic Analysis of Facial Actions: A Survey
Martínez, B., Valstar, M., Jiang, B., & Pantić, M. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2731763 · Cited by 370
Self-citationFacial 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
Sewa db: A rich database for audio-visual emotion and sentiment research in the wild
Kossaifi, J., Walecki, R., Panagakis, Y., Shen, J., Schmitt, M., Ringeval, F., Han, J., Pandit, V., Toisoul, A., Schuller, B., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2944808 · Cited by 243
Deep learning based FACS Action Unit occurrence and intensity estimation
Gudi, A., Tasli, H. E., den Uyl, T. M., & Maroulis, A. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7284873 · Cited by 178
(2015) · doi:10.1145/2808196.2811642 · Cited by 178
Self-citationDeep learning the dynamic appearance and shape of facial action units
Jaiswal, S., & Valstar, M. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477625 · Cited by 170
Self-citationSpatio-temporal convolutional features with nested LSTM for facial expression recognition
Yu, Z., Liu, G., Liu, Q., & Deng, J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.07.028 · Cited by 162
Action Unit Detection with Region Adaptation, Multi-labeling Learning and Optimal Temporal Fusing
Li, W., Abtahi, F., & Zhu, Z. (2017) · 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.2017.716 · Cited by 161
EAC-Net: Deep Nets with Enhancing and Cropping for Facial Action Unit Detection
Li, W., Abtahi, F., Zhu, Z., & Yin, L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2791608 · Cited by 151
Self-citationAdvances, Challenges, and Opportunities in Automatic Facial Expression Recognition
Martínez, B., & Valstar, M. (2016) · Springer eBooks · doi:10.1007/978-3-319-25958-1_4 · Cited by 140
Self-citationDeep Structured Learning for Facial Action Unit Intensity Estimation
Walecki, R., Rudovic, O. O., Pavlović, V., Schuller, B. W., & Pantić, M. (2017) · 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.2017.605 · Cited by 138
Facial Action Unit Detection Using Attention and Relation Learning
Shao, Z., Liu, Z., Cai, J., Wu, Y., & Ma, L. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2948635 · Cited by 132
EAC-Net: A Region-Based Deep Enhancing and Cropping Approach for Facial Action Unit Detection
Li, W., Abtahi, F., Zhu, Z., & Yin, L. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.136 · Cited by 131
Self-citationA comprehensive survey on automatic facial action unit analysis
Zhi, R., Liu, M., & Zhang, D. (2019) · The Visual Computer · doi:10.1007/s00371-019-01707-5 · Cited by 137
The NoXi database: multimodal recordings of mediated novice-expert interactions
Cafaro, A., Wagner, J., Baur, T., Dermouche, S., Torres, M. T., Pélachaud, C., André, E., & Valstar, M. (2017) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3136755.3136780 · Cited by 125
Self-citationFERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge
Valstar, M., Sánchez, E., Cohn, J. F., Jeni, L. A., Girard, J. M., Zhang, Z., Yin, L., & Pantić, M. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.107 · Cited by 129
Self-citationFace Behavior a la carte: Expressions, Affect and Action Units in a Single NetworkPreprint
Kollias, D., Sharmanska, V., & Zafeiriou, S. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.11111 · Cited by 228
Emotion recognition from facial expression using deep convolutional neural network
Liliana, D. Y. (2019) · Journal of Physics: Conference Series · doi:10.1088/1742-6596/1193/1/012004 · Cited by 113
Facial Action Unit Detection With Transformers
Jacob, G. M., & Stenger, B. (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/cvpr46437.2021.00759 · Cited by 114
Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face StudyPreprint
Kollias, D., Sharmanska, V., & Zafeiriou, S. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.03790 · Cited by 110
AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and ChallengePreprint
Valstar, M., Gratch, J. S., Schuller, B. W., Ringeval, F., Lalanne, D., Torres, M. T., Scherer, S. J., Stratou, G., Cowie, R., & Pantić, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.01600 · Cited by 129
Self-citationDeep convolutional BiLSTM fusion network for facial expression recognition
Liang, D., Liang, H., Yu, Z., & Zhang, Y. (2019) · The Visual Computer · doi:10.1007/s00371-019-01636-3 · Cited by 105
Incremental Boosting Convolutional Neural Network for Facial Action Unit RecognitionPreprint
Han, S., Meng, Z., Khan, A.-S., & Tong, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.05395 · Cited by 97
AU R-CNN: Encoding expert prior knowledge into R-CNN for action unit detection
Ma, C., Chen, L., & Yong, J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.03.082 · Cited by 100
A Review of Dynamic Datasets for Facial Expression Research
Krumhuber, E. G., Skora, L. I., Küster, D., & Fou, L. (2016) · Emotion Review · doi:10.1177/1754073916670022 · Cited by 101
Audio-visual emotion recognition using deep transfer learning and multiple temporal models
Ouyang, X., Kawaai, S., Goh, E. G. H., Shen, S., Ding, W., Ming, H., & Huang, D. (2017) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3136755.3143012 · Cited by 92
Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data
Jaiswal, S., Valstar, M., Gillott, A., & Daley, D. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.95 · Cited by 87
Self-citationChang, W.-Y., Hsu, S.-H., & Chien, J.-H. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.246 · Cited by 81
Deeper cascaded peak-piloted network for weak expression recognition
Yu, Z., Liu, Q., & Liu, G. (2017) · The Visual Computer · doi:10.1007/s00371-017-1443-0 · Cited by 79
Leo, M., Carcagnì, P., Mazzeo, P. L., Spagnolo, P., Cazzato, D., & Distante, C. (2020) · Information · doi:10.3390/info11030128 · Cited by 84
A Primer on Observational Measurement
Girard, J. M., & Cohn, J. F. (2016) · Assessment · doi:10.1177/1073191116635807 · Cited by 111
Self-citationOptimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition
Han, S., Meng, Z., Li, Z., O’Reilly, J. D., Cai, J., Wang, X., & Tong, Y. (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.2018.00532 · Cited by 71
Chhabra, G., Onyema, E. M., Kumar, S., Goutham, M., Mandapati, S., & Iwendi, C. (2022) · Electronics · doi:10.3390/electronics11234059 · Cited by 68
A multi-label convolutional neural network approach to cross-domain action unit detection
Ghosh, S., Laksana, E., Scherer, S. J., & Morency, L. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344632 · Cited by 61
Learning Facial Action Units from Web Images with Scalable Weakly Supervised Clustering
Zhao, K., Chu, W.-S., & Martı́nez, A. M. (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.2018.00223 · Cited by 62
Calvo, M. G., Fernández-Martín, A., Recio, G., & Lundqvist, D. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.02052 · Cited by 68
Zhang, Y., Dong, W., Hu, B.-G., & Ji, Q. (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.2018.00246 · Cited by 59
Self-Supervised Learning of Person-Specific Facial Dynamics for Automatic Personality Recognition
Song, S., Jaiswal, S., Sánchez, E., Tzimiropoulos, G., Shen, L., & Valstar, M. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3064601 · Cited by 57
Self-citationAudio and face video emotion recognition in the wild using deep neural networks and small datasets
Ding, W., Xu, M., Huang, D., Lin, W., Dong, M., Yu, X., & Li, H. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2997637 · Cited by 51
Facial Expression Recognition Based on Improved LeNet-5 CNN
Wang, G., & Gong, J. (2019) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2019.8832535 · Cited by 54
Discriminant multi-label manifold embedding for facial Action Unit detection
Yüce, A., Gao, H., & Thiran, J. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7284871 · Cited by 50
Facial Action Unit intensity prediction via Hard Multi-Task Metric Learning for Kernel Regression
Nicolle, J., Bailly, K., & Chétouani, M. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7284868 · Cited by 49
BAUM-2: a multilingual audio-visual affective face database
Erdem, Ç. E., Turan, C., & Aydın, Z. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-1986-2 · Cited by 49
Gender Estimation Based on Smile-Dynamics
Dantcheva, A., & Brémond, F. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2632070 · Cited by 49
Bilateral Ordinal Relevance Multi-instance Regression for Facial Action Unit Intensity Estimation
Zhang, Y., Zhao, R. Q., Dong, W., Hu, B.-G., & Ji, Q. (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.2018.00735 · Cited by 49
DeepCoder: Semi-Parametric Variational Autoencoders for Automatic Facial Action Coding
Tran, D. L., Walecki, R., Rudovic, O. O., Eleftheriadis, S., Schuller, B. W., & Pantić, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.346 · Cited by 49
Sun, B., Li, L., Wu, X., Zuo, T., Chen, Y., Zhou, G., He, J. Y. D., & Zhu, X. (2015) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-015-0203-6 · Cited by 46
Multi View Facial Action Unit Detection Based on CNN and BLSTM-RNN
He, J. Y. D., Li, D., Yang, B., Cao, S., Sun, B. C., & Yu, L. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.108 · Cited by 45
Facial Affect “In-the-Wild”: A Survey and a New Database
Zafeiriou, S. P., Papaioannou, A., Kotsia, I., Nicolaou, M. A., & Zhao, G. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.186 · Cited by 45
Sánchez, E., Tellamekala, M. K., Valstar, M., & Tzimiropoulos, G. (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/cvpr46437.2021.00896 · Cited by 43
Self-citationGeethanjali, R., & Valarmathi, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-73452-2 · Cited by 41
Pose-Independent Facial Action Unit Intensity Regression Based on Multi-Task Deep Transfer Learning
Zhou, Y., Pi, J., & Shi, B. E. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.112 · Cited by 41
Zia, M. S., Hussain, M., & Jaffar, M. A. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5806-y · Cited by 41
Zhang, Y., Jiang, H., Wu, B., Fan, Y., & Ji, Q. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00082 · Cited by 37
Expression-assisted facial action unit recognition under incomplete AU annotation
Wang, S., Gan, Q., & Ji, Q. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.07.028 · Cited by 39
A Novel Approach to Cross dataset studies in Facial Expression Recognition
Ramis, S., Rubio, J. M. B., Perales, F. J., & Manresa-Yee, C. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13117-2 · Cited by 45
Exemplar-based facial expression recognition
Farajzadeh, N., & Hashemzadeh, M. (2018) · Information Sciences · doi:10.1016/j.ins.2018.05.057 · Cited by 38
Pre-training Strategies and Datasets for Facial Representation Learning
Bulat, A., Cheng, S., Yang, J., Garbett, A., Sánchez, E., & Tzimiropoulos, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_7 · Cited by 38
Reliability and validity of machine vision for the assessment of facial expressions
Beringer, M., Spohn, F., Hildebrandt, A., Wacker, J., & Recio, G. (2019) · Cognitive Systems Research · doi:10.1016/j.cogsys.2019.03.009 · Cited by 40
Compound Facial Expression Recognition Based on Highway CNN
Slimani, K., Lekdioui, K., Messoussi, R., & Touahni, R. (2019) · Conference of the Moroccan Classification Society: New Challenges in Data Sciences · doi:10.1145/3314074.3314075 · Cited by 38
A Transfer Learning Approach to Heatmap Regression for Action Unit Intensity Estimation
Ντίνου, Ι., Sánchez, E., Bulat, A., Valstar, M., & Tzimiropoulos, G. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3061605 · Cited by 36
Self-citationMulti-Dimensional Classification via Decomposed Label Encoding
Jia, B.-B., & Zhang, M.-L. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3100436 · Cited by 36
Ming, Z., Bugeau, A., Rouas, J.-L., & Shochi, T. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7284870 · Cited by 35
Graph-Based Facial Affect Analysis: A Review
Liu, Y. M., Zhang, X., Li, Y., Zhou, J., Li, X., & Zhao, G. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3215918 · Cited by 42
Xiao, B., Imel, Z. E., Georgiou, P., Atkins, D. C., & Narayanan, S. S. (2016) · Current Psychiatry Reports · doi:10.1007/s11920-016-0682-5 · Cited by 36
View-Independent Facial Action Unit Detection
Tang, C., Zheng, W., Yan, J., Li, Q., Li, Y., Zhang, T., & Cui, Z. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.113 · Cited by 34
Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation
Zhang, Y., Wu, B., Dong, W., Li, Z., Liu, W., Hu, B.-G., & Ji, Q. (2019) · 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.2019.00357 · Cited by 33
Bian, Y., Küster, D., Liu, H., & Krumhuber, E. G. (2023) · Sensors · doi:10.3390/s24010126 · Cited by 39
A Multimodal Emotion Recognition System Using Facial Landmark Analysis
Rahdari, F., Rashedi, E., & Eftekhari, M. (2018) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-018-0142-9 · Cited by 32
Automatic Detection of a Driver’s Complex Mental States
Ma, Z., Mahmoud, M., Robinson, P., Dias, E., & Skrypchuk, L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-62398-6_48 · Cited by 31
Towards a Prediction and Data Driven Computational Process Model of Emotion
Scherer, K. R. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2905209 · Cited by 32
Deep Learning Based Affective Model for Speech Emotion Recognition
Zhou, X., Guo, J., & Bie, R. (2016) · 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld) · doi:10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0133 · Cited by 36
LGSNet: A Two-Stream Network for Micro- and Macro-Expression Spotting With Background Modeling
Yu, W.-W., Jiang, J., Yang, K.-F., Yan, H., & Li, Y. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3266808 · Cited by 35
Conditional adversarial synthesis of 3D facial action units
Liu, Z., Song, G., Cai, J., Cham, T., & Zhang, J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.05.003 · Cited by 30
Facial landmark localization: Past, present and future
Ouanan, H., Ouanan, M., & Aksasse, B. (2016) · IEEE Congress on Information Science and Technology (CiSt) · doi:10.1109/cist.2016.7805097 · Cited by 30
Deep Learning for Facial Action Unit Detection Under Large Head Poses
Tősér, Z., Jeni, L. A., Lörincz, A., & Cohn, J. F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8_29 · Cited by 29
Self-citationFEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation
Yan, Y., Lü, K., Xue, J., Gao, P., & Lyu, J. (2019) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2019.0-104 · Cited by 31
Hu, M. Q., Yang, C., Zheng, Y., Wang, X., He, L., & Ren, F. (2019) · IEEE Access · doi:10.1109/access.2019.2936976 · Cited by 29
Aamir, M., Ali, T. M., Shaf, A., Irfan, M., & Saleem, M. Q. (2020) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-020-04811-0 · Cited by 29
Iterative Distillation for Better Uncertainty Estimates in Multitask Emotion Recognition
Deng, D., Wu, L., & Shi, B. E. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00396 · Cited by 29
Shima, Y., & Omori, Y. (2018) · International Conference on Robotics, Control and Automation (ICRCA) · doi:10.1145/3265639.3265664 · Cited by 28
Tang, Y. S., Zeng, W., Zhao, D., & Zhang, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01266 · Cited by 29
Comparison of selected off-the-shelf solutions for emotion recognition based on facial expressions
Brodny, G., Kołakowska, A., Landowska, A., Szwoch, M., Szwoch, W., & Wróbel, M. R. (2016) · Conference on Human System Interactions · doi:10.1109/hsi.2016.7529664 · Cited by 28
Faces of Focus: A Study on the Facial Cues of Attentional States
Babaei, E., Srivastava, N., Newn, J., Zhou, Q., Dingler, T., & Velloso, E. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376566 · Cited by 27
Facial Action Unit Recognition and Intensity Estimation Enhanced Through Label Dependencies
Wang, S., Hao, L., & Ji, Q. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2878339 · Cited by 26
Toward Robust Facial Action Units’ Detection
Yang, J., Hristov, Y., Shen, J., Lin, Y., & Pantić, M. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3257542 · Cited by 26
Action Units and Their Cross-Correlations for Prediction of Cognitive Load during Driving
Yüce, A., Gao, H., Cuendet, G. L., & Thiran, J. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2584042 · Cited by 25
Predicting the distribution of emotion perception: capturing inter-rater variability
Zhang, B., Essl, G., & Provost, E. M. (2017) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3136755.3136792 · Cited by 27
Improved TOPSIS method for peak frame selection in audio-video human emotion recognition
Singh, L., Singh, S., & Aggarwal, N. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6402-x · Cited by 25
A Survey of the Trends in Facial and Expression Recognition Databases and Methods
Roychowdhury, S., & Emmons, M. (2015) · International Journal of Computer Science & Engineering Survey · doi:10.5121/ijcses.2015.6501 · Cited by 23
Female, white, 27? Bias Evaluation on Data and Algorithms for Affect Recognition in Faces
Pahl, J., Rieger, I., Möller, A., Wittenberg, T. C., & Schmid, U. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533159 · Cited by 26
Detecting Autism by Analyzing a Simulated Social Interaction
Drimalla, H., Landwehr, N., Baskow, I., Behnia, B., Roepke, S., Dziobek, I., & Scheffer, T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10925-7_12 · Cited by 23
On line emotion detection using retrainable deep neural networks
Kollias, D., Tagaris, A., & Stafylopatis, A. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7850049 · Cited by 22
Metadata
- Authors
- Valstar, M. F., Almaev, T., Girard, J. M., McKeown, G., Mehu, M., Yin, L., Pantic, M., & Cohn, J. F.
- Affiliations
- University of Nottingham (GB)
- University of Pittsburgh (US)
- Queen's University Belfast (GB)
- Binghamton University (US)
- Venue
- 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)
- Publisher
- IEEE
- Published
- 2015-05
- Pages
- 1-8
- ISSN
- 1541-5058 (ISSN-L)
- OpenAlex
- W1655469623
- Cited by
- 252
- References
- 25
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 12 · 2023: 7 · 2022: 11 · 2021: 31 · 2020: 17 · 2019: 39 · 2018: 32 · 2017: 41 · 2016: 33 · 2015: 19
- FWCI
- 55.51
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Automatic Face and Gesture Recognition (publishedVersion)
- Research Portal (Queen's University Belfast) (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, facial expression analysis, action unit detection, benchmark dataset, evaluation protocol
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
