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triplet loss, class-aware margin, outlier suppression,
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Facial expression recognition, Deep learning,
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Face recognition, Adaptation models, Training,
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Face recognition, Feature extraction, Task analysis, Training, Faces,
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Facial expression recognition, Deep learning, Mask RCNN,
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Facial expression recognition, Discriminatively deep fusion approach,
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Optimization, Face recognition, Deep learning, Faces,
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2303
Deep learning, Emotion recognition, Image recognition, Databases,
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Feature extraction, Databases, Face recognition, Semantics,
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Training, Visualization, Law, Psychology, Machine learning,
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Deep learning, Databases, Face recognition,
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Tjiputra, E.[Erman],
Le, B.[Bac],
Nguyen, A.[Anh],
Uncertainty-aware Label Distribution Learning for Facial Expression
Recognition,
WACV23(6077-6086)
IEEE DOI
2302
Training, Learning systems, Deep learning, Adaptation models,
Emotion recognition, Uncertainty, Face recognition, body pose
BibRef
Bonnard, J.[Jules],
Dapogny, A.[Arnaud],
Dhombres, F.[Ferdinand],
Bailly, K.[Kevin],
Privileged Attribution Constrained Deep Networks for Facial
Expression Recognition,
ICPR22(1055-1061)
IEEE DOI
2212
Heating systems, Location awareness, Adaptation models,
Ultrasonic imaging, Limiting, Face recognition, Computational modeling
BibRef
Phan, K.N.[Kim Ngan],
Nguyen, H.H.[Hong-Hai],
Huynh, V.T.[Van-Thong],
Kim, S.H.[Soo-Hyung],
Facial Expression Classification using Fusion of Deep Neural Network
in Video,
ABAW22(2506-2510)
IEEE DOI
2210
Human computer interaction, Emotion recognition,
Computational modeling, Neural networks, Transformers,
Pattern recognition
BibRef
Li, H.[Hangyu],
Wang, N.N.[Nan-Nan],
Yang, X.[Xi],
Wang, X.Y.[Xiao-Yu],
Gao, X.B.[Xin-Bo],
Towards Semi-Supervised Deep Facial Expression Recognition with An
Adaptive Confidence Margin,
CVPR22(4156-4165)
IEEE DOI
2210
Training, Adaptation models, Codes, Face recognition,
Semisupervised learning, Data models, Face and gestures,
Self- semi- meta- unsupervised learning
BibRef
Schoneveld, L.[Liam],
Othmani, A.[Alice],
Towards a General Deep Feature Extractor for Facial Expression
Recognition,
ICIP21(2339-2342)
IEEE DOI
2201
Training, Deep learning, Emotion recognition, Visualization,
Image recognition, Face recognition,
Knowledge distillation
BibRef
Zheng, Z.Z.[Zhen-Zhu],
Rasmussen, C.[Christopher],
Peng, X.[Xi],
Student-Teacher Oneness: A Storage-efficient approach that improves
facial expression recognition,
HTCV21(4060-4069)
IEEE DOI
2112
Training, Lead acid batteries, Deep learning,
Face recognition, Memory management
BibRef
Farzaneh, A.H.[Amir Hossein],
Qi, X.J.[Xiao-Jun],
Facial Expression Recognition in the Wild via Deep Attentive Center
Loss,
WACV21(2401-2410)
IEEE DOI
2106
Measurement, Learning systems, Face recognition,
Neural networks, Linear programming
BibRef
Méndez-Llanes, N.[Nelson],
Castillo-Rosado, K.[Katy],
Méndez-Vázquez, H.[Heydi],
Tistarelli, M.[Massimo],
Quality-based Representation for Unconstrained Face Recognition,
ICPR21(6494-6500)
IEEE DOI
2105
Deep learning, Analytical models, Databases, Face recognition,
Computational modeling, Feature extraction, Proposals
BibRef
Kondo, K.[Kazuaki],
Nakamura, T.[Taichi],
Nakamura, Y.[Yuichi],
Satoh, S.[Shinichi],
Siamese-structure Deep Neural Network Recognizing Changes in Facial
Expression According to the Degree of Smiling,
ICPR21(4605-4612)
IEEE DOI
2105
Measurement, Visualization, Image recognition, Face recognition,
Perturbation methods, Neural networks, Mouth, Quality-of-life,
Siamese network
BibRef
Valev, H.[Hristo],
Gallucci, A.[Alessio],
Leufkens, T.[Tim],
Westerink, J.[Joyce],
Sas, C.[Corina],
Applying Delaunay Triangulation Augmentation for Deep Learning Facial
Expression Generation and Recognition,
FBE20(730-740).
Springer DOI
2103
BibRef
Ekundayo, O.[Olufisayo],
Viriri, S.[Serestina],
Facial Expression Recognition and Ordinal Intensity Estimation:
A Multilabel Learning Approach,
ISVC20(II:581-592).
Springer DOI
2103
BibRef
Earlier:
Deep Forest Approach for Facial Expression Recognition,
PSIVT19(149-161).
Springer DOI
2003
BibRef
Do, N.T.,
Nguyen-Quynh, T.T.,
Kim, S.H.,
Affective Expression Analysis in-the-wild using Multi-Task Temporal
Statistical Deep Learning Model,
FG20(624-628)
IEEE DOI
2102
Face recognition, Emotion recognition, Training, Testing,
Feature extraction, Faces, Annotations,
ABAW Challenge
BibRef
Rasipuram, S.,
Bhat, J.H.,
Maitra, A.,
Multi-modal Sequence-to-sequence Model for Continuous Affect
Prediction in the Wild Using Deep 3D Features,
FG20(611-614)
IEEE DOI
2102
Feature extraction, Videos, Face recognition, Visualization,
Databases, Emotion recognition, Training, affect recognition,
multi modal anaysis
BibRef
Bernheim, S.,
Arnaud, E.,
Dapogny, A.[Arnaud],
Bailly, K.[Kevin],
MoDuL: Deep Modal and Dual Landmark-wise Gated Network for Facial
Expression Recognition,
FG20(153-159)
IEEE DOI
2102
Logic gates, Feature extraction, Face recognition, Agriculture,
Faces, Task analysis, Iron, Facial expression recognition,
ensemble methods
BibRef
Kuo, C.,
Lai, S.,
Sarkis, M.,
A Compact Deep Learning Model for Robust Facial Expression
Recognition,
AMFG18(2202-22028)
IEEE DOI
1812
Databases, Training, Face recognition, Hidden Markov models, Face,
Image recognition, Image sequences
BibRef
Mavani, V.,
Raman, S.,
Miyapuram, K.P.,
Facial Expression Recognition Using Visual Saliency and Deep Learning,
CogCV17(2783-2788)
IEEE DOI
1802
Face recognition, Feature extraction, Machine learning, Testing,
Training, Visualization
BibRef
Liu, X.F.[Xiao-Feng],
Kumar, B.V.K.V.[B.V.K. Vijaya],
You, J.[Jane],
Jia, P.[Ping],
Adaptive Deep Metric Learning for Identity-Aware Facial Expression
Recognition,
Biometrics17(522-531)
IEEE DOI
1709
Face recognition, Feature extraction, Measurement,
Optimization, Training
BibRef
Ding, H.,
Zhou, S.K.,
Chellappa, R.,
FaceNet2ExpNet:
Regularizing a Deep Face Recognition Net for Expression Recognition,
FG17(118-126)
IEEE DOI
1707
Convolution, Distribution functions, Face, Face recognition,
Image recognition, Neurons, Training
BibRef
Ghasemi, A.,
Baktashmotlagh, M.,
Denman, S.,
Sridharan, S.,
Tien, D.N.,
Fookes, C.,
Deep discovery of facial motions using a shallow embedding layer,
ICIP17(1567-1571)
IEEE DOI
1803
Feature extraction, Hidden Markov models, Kernel, Machine learning,
Pain, Training
BibRef
Ghasemi, A.,
Denman, S.[Simon],
Sridharan, S.[Sridha],
Fookes, C.[Clinton],
Discovery of facial motions using deep machine perception,
WACV16(1-7)
IEEE DOI
1606
Convolution
BibRef
Karali, A.[Abubakrelsedik],
Bassiouny, A.[Ahmad],
El-Saban, M.[Motaz],
Facial expression recognition in the wild using rich deep features,
ICIP15(3442-3446)
IEEE DOI
1512
Facial expression recognition; deep neural networks features
BibRef
Liu, P.[Ping],
Han, S.Z.[Shi-Zhong],
Meng, Z.[Zibo],
Tong, Y.[Yan],
Facial Expression Recognition via a Boosted Deep Belief Network,
CVPR14(1805-1812)
IEEE DOI
1409
BibRef
Liu, P.[Ping],
Zhou, J.T.Y.[Joey Tian-Yi],
Tsang, I.W.H.[Ivor Wai-Hung],
Meng, Z.[Zibo],
Han, S.Z.[Shi-Zhong],
Tong, Y.[Yan],
Feature Disentangling Machine: A Novel Approach of Feature Selection
and Disentangling in Facial Expression Analysis,
ECCV14(IV: 151-166).
Springer DOI
1408
BibRef
Li, W.[Wei],
Li, M.[Min],
Su, Z.[Zhong],
Zhu, Z.G.[Zhi-Gang],
A Deep-Learning Approach to Facial Expression Recognition with Candid
Images,
MVA15(279-282)
IEEE DOI
1507
Computational modeling
BibRef
Jung, H.[Heechul],
Lee, S.[Sihaeng],
Yim, J.,
Park, S.[Sunjeong],
Kim, J.[Junmo],
Joint Fine-Tuning in Deep Neural Networks for Facial Expression
Recognition,
ICCV15(2983-2991)
IEEE DOI
1602
Databases
BibRef
Jung, H.[Heechul],
Lee, S.[Sihaeng],
Park, S.[Sunjeong],
Kim, B.J.[Byung-Ju],
Kim, J.[Junmo],
Lee, I.[Injae],
Ahn, C.H.[Chung-Hyun],
Development of deep learning-based facial expression recognition
system,
FCV15(1-4)
IEEE DOI
1506
Haar transforms
BibRef
Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Sptatio-Temporal Analysis for Face Expression Recognition .