The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression
Abstract
In 2000, the Cohn-Kanade (CK) database was released for the purpose of promoting research into automatically detecting individual facial expressions. Since then, the CK database has become one of the most widely used test-beds for algorithm development and evaluation. During this period, three limitations have become apparent: 1) While AU codes are well validated, emotion labels are not, as they refer to what was requested rather than what was actually performed, 2) The lack of a common performance metric against which to evaluate new algorithms, and 3) Standard protocols for common databases have not emerged. As a consequence, the CK database has been used for both AU and emotion detection (even though labels for the latter have not been validated), comparison with benchmark algorithms is missing, and use of random subsets of the original database makes meta-analyses difficult. To address these and other concerns, we present the Extended Cohn-Kanade (CK+) database. The number of sequences is increased by 22% and the number of subjects by 27%. The target expression for each sequence is fully FACS coded and emotion labels have been revised and validated. In addition to this, non-posed sequences for several types of smiles and their associated metadata have been added. We present baseline results using Active Appearance Models (AAMs) and a linear support vector machine (SVM) classifier using a leave-one-out subject cross-validation for both AU and emotion detection for the posed data. The emotion and AU labels, along with the extended image data and tracked landmarks will be made available July 2010.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Statistical Methods for Rates and Proportions
Fleiss, J. L., Levin, B., & Paik, M. C. (2003) · Wiley Series in Probability and Statistics · doi:10.1002/0471445428 · Cited by 18,646
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,123
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409
A Practical Guide to Support Vector Classication
Hsu, C., Chang, C.-C., & Lin, C. (2008) · Cited by 5,056
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
Comprehensive database for facial expression analysis
Takeo Kanade, A., Cohn, J. F., & Tian, Y. (2002) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2000.840611 · Cited by 2,637
Self-citationStatistical Methods for Rates and Proportions.
Plewis, I., & Fleiss, J. L. (1982) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2982107 · Cited by 1,753
Active Appearance Models Revisited
Matthews, I., & Baker, S. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029666.37597.d3 · Cited by 1,538
Self-citationSocial signal processing: Survey of an emerging domain
Vinciarelli, A., Pantic, M., & Bourlard, H. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.11.007 · Cited by 842
Flagged first, then by citations
Research works printed but not linked: 2
- gross, 2007, The CMU Multiple Pose, Illumination, and Expression (MultiPIE), Technical Report 16 Robotics Institute Carnegie Mellon University
- ekman, 2002, Facial Action Coding System: Research Nexus, Network Research Information
Cited by
Sorted by citations
Items 901–1,000 of 1,009
Emerging Frontiers in Human–Robot Interaction
Safavi, F., Olikkal, P., Pei, D., Kamal, S., Meyerson, H., Penumalee, V., & Vinjamuri, R. K. (2024) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-024-02074-7 · Cited by 33
Zaman, K., Zengkang, G., Sun, Z., Shah, S. M., Riaz, W., Ji, J., Hussain, T., & Attar, R. W. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/6611276 · Cited by 31
Manifold based sparse representation for facial understanding in natural images
Ptucha, R., & Savakis, A. E. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2013.03.003 · Cited by 31
Toward Use of Facial Thermal Features in Dynamic Assessment of Affect and Arousal Level
Khan, M. M., Ward, R. D., & Ingleby, M. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2535291 · Cited by 31
Deep Learning Based Real-Time Driver Emotion Monitoring
Verma, B., & Choudhary, A. (2018) · IEEE International Conference on Vehicular Electronics and Safety (ICVES) · doi:10.1109/icves.2018.8519595 · Cited by 31
Graph-based dynamic ensemble pruning for facial expression recognition
Li, D., Wen, G., Xu, L., & Cai, X. (2019) · Applied Intelligence · doi:10.1007/s10489-019-01435-2 · Cited by 32
Driver Emotion Recognition for Enhancement of Human Machine Interface in Vehicles
Patil, M., & Veni, S. (2019) · International Conference on Communications and Signal Processing (ICCSP) · doi:10.1109/iccsp.2019.8698045 · Cited by 31
Histogram distance metric learning for facial expression recognition
Sadeghi, H., & Raie, A. A. (2019) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2019.05.004 · Cited by 31
FEAFA: 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
Recognition of Facial Expressions under Varying Conditions Using Dual‐Feature Fusion
Mahmood, A., Hussain, S., Iqbal, K., & Elkilani, W. S. (2019) · Mathematical Problems in Engineering · doi:10.1155/2019/9185481 · Cited by 31
McDuff, D., Jun, E., Rowan, K., & Czerwinski, M. P. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2961912 · Cited by 31
Dini, H., Ghassemi, F., & Sendi, M. S. E. (2020) · Brain Topography · doi:10.1007/s10548-020-00794-1 · Cited by 31
Alphonse, A. S., Shankar, K., Rakkini, M. J. J., Ananthakrishnan, S., Athisayamani, S., Singh, A. R., & Ramasamy, G. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02517-7 · Cited by 31
RetractedSeyyedsalehi, S. Z., & Seyyedsalehi, S. A. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.05.057 · Cited by 31
AI-based Behavioural Analyser for Interviews/Viva
Dissanayake, D. Y., Amalya, V., Dissanayaka, R., Lakshan, L., Samarasinghe, P. D., Nadeeshani, M., & Samarasinghe, P. (2021) · IEEE International Conference on Industrial and Information Systems (ICIIS) · doi:10.1109/iciis53135.2021.9660757 · Cited by 31
Region Attention Enhanced Unsupervised Cross-Domain Facial Emotion Recognition
Ji, Y., Hu, Y., Yang, Y., & Shen, H. T. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3136606 · Cited by 31
Parra-Dominguez, G. S., Garcia-Capulin, C. H., & Sánchez-Yáñez, R. E. (2022) · Diagnostics · doi:10.3390/diagnostics12071528 · Cited by 31
Banerjee, A., Mutlu, O. C., Kline, A., Surabhi, S., Washington, P. Y., & Wall, D. P. (2022) · JMIR Formative Research · doi:10.2196/39917 · Cited by 35
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
2D Segmentation Using a Robust Active Shape Model With the EM Algorithm
Santiago, C., Nascimento, J. C., & Marques, J. S. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2424311 · Cited by 31
Lee, S. H., & Ro, Y. M. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2496320 · Cited by 31
Learning finite Beta-Liouville mixture models via variational bayes for proportional data clustering
Fan, W., & Bouguila, N. (2013) · International Joint Conference on Artificial Intelligence · Cited by 31
Learning with privileged information for multi-Label classification
Wang, S., Chen, S., Chen, T., & Shi, X. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.03.033 · Cited by 31
A Machine Learning Emotion Detection Platform to Support Affective Well Being
Healy, M., Donovan, R., Walsh, P., & Zheng, H. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621562 · Cited by 31
Kortelainen, J., Tiinanen, S., Huang, X., Li, X., Laukka, S., Pietikainen, M., & Seppanen, T. (2012) · 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc.2012.6347175 · Cited by 30
Multi-modal emotion identification fusing facial expression and EEG
Wu, Y., & Li, J. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13711-4 · Cited by 32
Facial expression recognition based on emotional artificial intelligence for tele-rehabilitation
Ciraolo, D., Fazio, M. C., Calabrò, R. S., Villari, M., & Celesti, A. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106096 · Cited by 30
Decoding Human Facial Emotions: A Ranking Approach Using Explainable AI
Punuri, S. B., Kuanar, S. K., Mishra, T. K., Rao, V. V. R. M., & Reddy, S. S. (2024) · IEEE Access · doi:10.1109/access.2024.3474012 · Cited by 30
LGE-KSVD: Robust Sparse Representation Classification
Ptucha, R., & Savakis, A. E. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2303648 · Cited by 30
Spatio-temporal Event Classification Using Time-Series Kernel Based Structured Sparsity
Jeni, L. A., Lörincz, A., Szabó, Z., Cohn, J. F., & Takeo Kanade, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10593-2_10 · Cited by 30
Self-citationAutomatic Facial Expression Recognition
Whitehill, J., Bartlett, M. S., & Movellan, J. R. (2013) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195387643.003.0007 · Cited by 30
Ming, Z., Chazalon, J., Luqman, M. M., Visani, M., & Burie, J.-C. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545274 · Cited by 32
Facial Expression Recognition Based on Auxiliary Models
Wang, Y., Li, Y., Song, Y. H., & Rong, X. (2019) · Algorithms · doi:10.3390/a12110227 · Cited by 30
Buršić, S., Boccignone, G., Ferrara, A., D’Amelio, A., & Lanzarotti, R. (2020) · Applied Sciences · doi:10.3390/app10114002 · Cited by 30
Porcu, S., Floris, A., Voigt‐Antons, J., Atzori, L., & Möller, S. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3018303 · Cited by 30
FENP: A Database of Neonatal Facial Expression for Pain Analysis
Yan, J., Lu, G., Li, X., Zheng, W., Huang, C., Cui, Z., Zong, Y., Chen, M., Hao, Q., Liu, Y., et al. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3030296 · Cited by 30
Saurav, S., Saini, R., & Singh, S. K. (2021) · IEEE Access · doi:10.1109/access.2021.3108029 · Cited by 31
Zhu, X., He, Z., Zhao, L., Dai, Z., & Yang, Q. (2022) · Sensors · doi:10.3390/s22041350 · Cited by 30
When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework
Zou, X., Yan, Y., Xue, J., Chen, S., & Wang, H. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i5.20474 · Cited by 30
A randomized deep neural network for emotion recognition with landmarks detection
Luzio, F. D., Rosato, A., & Panella, M. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104418 · Cited by 31
KTFEv2: Multimodal Facial Emotion Database and its Analysis
Nguyen, H. T., Tran, N., Nguyen, H. D., Nguyen, H. T. L., & Kotani, K. (2023) · IEEE Access · doi:10.1109/access.2023.3246047 · Cited by 30
Mao, Y., & Liu, Y. (2023) · Scientific Reports · doi:10.1038/s41598-023-30442-0 · Cited by 32
Few-shot learning for facial expression recognition: a comprehensive survey
Kim, C.-L., & Kim, B. (2023) · Journal of Real-Time Image Processing · doi:10.1007/s11554-023-01310-x · Cited by 32
FER-CHC: Facial expression recognition with cross-hierarchy contrast
Wu, X., He, J., Huang, Q., Huang, C., Zhu, J., Huang, X., & Fujita, H. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110530 · Cited by 30
Garbas, J.-U., Ruf, T., Unfried, M., & Dieckmann, A. (2013) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2013.100 · Cited by 30
Mariappan, M. B., Suk, M., & Prabhakaran, B. (2012) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2012.24 · Cited by 30
Inferring Visual Persuasion via Body Language, Setting, and Deep Features
Huang, X., & Kovashka, A. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.102 · Cited by 30
Boosting Hankel matrices for face emotion recognition and pain detection
Presti, L. L., & Cascia, M. L. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.10.007 · Cited by 30
Shabat, A. M., & Tapamo, J. (2018) · IET Computer Vision · doi:10.1049/iet-cvi.2017.0340 · Cited by 30
Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories
Otberdout, N., Kacem, A., Daoudi, M., Ballihi, L., & Berretti, S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2947244 · Cited by 30
Measuring Student Engagement Level Using Facial Information
Alkabbany, I. M., Ali, A., Farag, A. A., Bennett, I., Ghanoum, M., & Farag, A. A. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803590 · Cited by 30
Improved Methods for Automatic Facial Expression Recognition
Echoukairi, H., Ghmary, M. E., Ziani, S., & Ouacha, A. (2023) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v17i06.37031 · Cited by 29
Trusted emotion recognition based on multiple signals captured from video
Zhang, J., Zheng, K., Mazhar, S., Fu, X., & Kong, J. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120948 · Cited by 29
STT-Net: Simplified Temporal Transformer for Emotion Recognition
Khan, M., Saddik, A. E., Deriche, M. A., & Gueaieb, W. (2024) · IEEE Access · doi:10.1109/access.2024.3413136 · Cited by 29
FSTL-SA: few-shot transfer learning for sentiment analysis from facial expressions
Meena, G., Mohbey, K. K., & Lokesh, K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20518-y · Cited by 29
Dynamic facial expression recognition using local patch and LBP-TOP
Wang, Y., Yu, H. L., Stevens, B., & Liu, H. (2015) · Conference on Human System Interactions · doi:10.1109/hsi.2015.7170694 · Cited by 29
FEEDB: A multimodal database of facial expressions and emotions
Szwoch, M. (2013) · Conference on Human System Interactions · doi:10.1109/hsi.2013.6577876 · Cited by 29
Teaching emotion expressions to a human companion robot using deep neural architectures
Churamani, N., Kerzel, M., Strahl, E., Barros, P. V. A., & Wermter, S. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7965911 · Cited by 29
Facial emotion classification using concatenated geometric and textural features
Sen, D., Datta, S., & Raman, B. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6537-9 · Cited by 30
A Graph-Structured Representation with BRNN for Static-based Facial Expression Recognition
Zhong, L., Bai, C., Li, J., Chen, T., Li, S., & Liu, Y. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756615 · Cited by 29
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
Public database for validation of follicle detection algorithms on 3D ultrasound images of ovaries
Potočnik, B., Munda, J., Reljič, M., Rakić, K., Knez, J., Vlaisavljević, V., Sedej, G., Cigale, B., Holobar, A., & Zazula, D. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105621 · Cited by 29
Detecting Learner Engagement in MOOCs using Automatic Facial Expression Recognition
Abhilash, D., & Gopalan, A. (2020) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon45650.2020.9125149 · 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
Separable convolutional neural networks for facial expressions recognition
Chowanda, A. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00522-x · Cited by 29
Emotion Recognition Based on Body and Context Fusion in the Wild
Huang, Y., Wen, H., Qing, L., Jin, R., & Xiao, L. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00403 · Cited by 29
An intelligent facial expression recognition system with emotion intensity classification
Saxena, S., Tripathi, S., & Sudarshan, T. S. B. (2022) · Cognitive Systems Research · doi:10.1016/j.cogsys.2022.04.001 · Cited by 29
FaceListener: Recognizing Human Facial Expressions via Acoustic Sensing on Commodity Headphones
Song, X., Huang, K., & Gao, W. (2022) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn54338.2022.00019 · Cited by 29
Discussions of Different Deep Transfer Learning Models for Emotion Recognitions
Yen, C.-T., & Li, K.-H. (2022) · IEEE Access · doi:10.1109/access.2022.3209813 · Cited by 29
MGEED: A Multimodal Genuine Emotion and Expression Detection Database
Wang, Y., Yu, H. L., Gao, W., Xia, Y., & Nduka, C. C. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3286351 · Cited by 29
Evolutionary Neural Architecture Search for Facial Expression Recognition
Deng, S., Lv, Z., Galván, E., & Sun, Y. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3289974 · Cited by 30
Dual subspace manifold learning based on GCN for intensity-invariant facial expression recognition
Chen, J., Shi, J., & Xu, R. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110157 · Cited by 29
Intensity Rank Estimation of Facial Expressions Based on a Single Image
Chang, K.-Y., Chen, C., & Hung, Y. (2013) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2013.538 · Cited by 29
Facial Expression Recognition with Occlusions Based on Geometric Representation
Cornejo, J. Y. R., Pedrini, H., & Flórez‐Revuelta, F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25751-8_32 · Cited by 29
Liu, C., Calvo, R. A., & Lim, R. (2016) · Frontiers in ICT · doi:10.3389/fict.2016.00011 · Cited by 29
A Simple Geometric-Based Descriptor for Facial Expression Recognition
Acevedo, D. G., Negri, P., Buemi, M. E., Fernández, F. G., & Mejail, M. E. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.101 · Cited by 29
Syeda, U. H., Zafar, Z., Islam, Z. Z., Tazwar, S. M., Rasna, M. J., Kise, K., & Ahad, M. A. R. (2017) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3123024.3125618 · Cited by 29
Fatigue Detection Using Facial Landmarks
Irtija, N., Mahsius, S. A. M. I., & Ahad, M. A. R. (2018) · International Symposium on Affective Science and Engineering · doi:10.5057/isase.2018-c000041 · Cited by 29
Kwong, J. C. T., Garcia, F. C. C., Abu, P. A. R., & Reyes, R. S. J. (2018) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2018.8650192 · Cited by 30
A Hyper-Attentive Multimodal Transformer for Real-Time and Robust Facial Expression Recognition
Tagmatova, Z., Umirzakova, S., Kutlimuratov, A., Abdusalomov, A. B., & Cho, Y. I. (2025) · Applied Sciences · doi:10.3390/app15137100 · Cited by 28
Schmidtmann, G., Jennings, B. J., Sandra, D. A., Pollock, J., & Gold, I. (2020) · Perception · doi:10.1177/0301006620901671 · Cited by 28
The Complex Emotion Expression Database: A validated stimulus set of trained actors
Benda, M. S., & Scherf, K. S. (2020) · PLoS ONE · doi:10.1371/journal.pone.0228248 · Cited by 33
UIBVFED: Virtual facial expression dataset
Mascaró-Oliver, M., & Amengual, E. (2020) · PLoS ONE · doi:10.1371/journal.pone.0231266 · Cited by 28
Facial Expression Recognition Based on Graph Neural Network
Xu, X., Ruan, Z., & Yang, L. (2020) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc50857.2020.9177430 · Cited by 28
Contour and region harmonic features for sub-local facial expression recognition
Shahid, A. R., Khan, S., & Yan, H. (2020) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2020.102949 · Cited by 28
Real-time face alignment: evaluation methods, training strategies and implementation optimization
Casado, C. Á., & López, M. B. (2021) · Journal of Real-Time Image Processing · doi:10.1007/s11554-021-01107-w · Cited by 28
Multimodal Emotion Recognition Using Different Fusion Techniques
Subramanian, G., Cholendiran, N., Prathyusha, K., Balasubramanain, N., & Aravinth, J. (2021) · International Conference on Bio Signals, Images, and Instrumentation (ICBSII) · doi:10.1109/icbsii51839.2021.9445146 · Cited by 28
Facial Expressions as a Vulnerability in Face Recognition
Peña, A., Morales, A., Serna, I., Fiérrez, J., & Lapedriza, À. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506444 · Cited by 28
Survey on Facial Expression Recognition: History, Applications, and Challenges
Zhao, X., Zhu, J., Luo, B., & Gao, Y. (2021) · IEEE Multimedia · doi:10.1109/mmul.2021.3107862 · Cited by 28
Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks
Haghpanah, M. A., Saeedizade, E., Masouleh, M. T., & Kalhor, A. (2022) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/mvip53647.2022.9738754 · Cited by 28
Wadhawan, R., & Gandhi, T. K. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3172272 · Cited by 28
Facial expression recognition based on improved depthwise separable convolutional network
Huo, H., Yu, Y., & Liu, Z. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14066-6 · Cited by 28
Academic Emotion Classification Using FER: A Systematic Review
Lek, J. X.-Y., & Teo, J. (2023) · Human Behavior and Emerging Technologies · doi:10.1155/2023/9790005 · Cited by 30
An Efficient Approach to Face Emotion Recognition with Convolutional Neural Networks
Białek, C., Matiolański, A., & Grega, M. (2023) · Electronics · doi:10.3390/electronics12122707 · Cited by 28
Ning, X., Feng, H., Dong, X., Li, W., Alenezi, F. S., & Tiwari, P. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120130 · Cited by 28
Automatic facial attribute analysis via adaptive sparse representation of random patches
Mery, D., & Bowyer, K. W. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.05.005 · Cited by 28
A Psychologically-Inspired Match-Score Fusion Model for Video-Based Facial Expression Recognition
Cruz, A. C., Bhanu, B., & Yang, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24571-8_45 · Cited by 28
Random forest-based feature selection for emotion recognition
Gharsalli, S., Emile, B., Laurent, H., Desquesnes, X., & Vivet, D. (2015) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2015.7367144 · Cited by 28
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
Real time facial expression recognition app development on mobile phones
Alshamsi, H., Meng, H., & Li, M. (2016) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2016.7603442 · Cited by 28
Metadata
- Authors
- Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I.
- Affiliations
- University of Pittsburgh (US)
- Carnegie Mellon University (US)
- Walt Disney (United States) (US)
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 94-101
- ISSN
- 2160-7508 (ISSN-L)
- OpenAlex
- W2103943262
- Cited by
- 4,303
- References
- 29
- Citations by year
- 2026: 139 · 2025: 286 · 2024: 350 · 2023: 462 · 2022: 440 · 2021: 464 · 2020: 411 · 2019: 397 · 2018: 332 · 2017: 268 · 2016: 214 · 2015: 199 · 2014: 139 · 2013: 110 · 2012: 53
- FWCI
- 121.79
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (publishedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health
- 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 action units, emotion recognition, facial landmark tracking, active appearance models, support vector classifier, benchmark dataset
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
