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Emotion recognition from mid-level features,
PRL(67, Part 1), No. 1, 2015, pp. 66-74.
Elsevier DOI
1511
Facial expression
BibRef
Pons, G.[Gerard],
Masip, D.[David],
Multitask, Multilabel, and Multidomain Learning With Convolutional
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Cyber(52), No. 6, June 2022, pp. 4764-4771.
IEEE DOI
2207
Task analysis, Emotion recognition, Training, Image recognition,
Face recognition, Deep learning, multitask learning
BibRef
Kaya, H.[Heysem],
Gürpinar, F.[Furkan],
Salah, A.A.[Albert Ali],
Video-based emotion recognition in the wild using deep transfer
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IVC(65), No. 1, 2017, pp. 66-75.
Elsevier DOI
1709
EmotiW
BibRef
Kim, H.,
Kim, Y.,
Kim, S.J.,
Lee, I.,
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MultMed(20), No. 11, November 2018, pp. 2980-2992.
IEEE DOI
1810
Feature extraction, Emotion recognition, Machine learning,
Predictive models, Databases, Task analysis, Psychology,
deep network
BibRef
Lingenfelser, F.,
Wagner, J.,
Deng, J.,
Brueckner, R.,
Schuller, B.,
André, E.,
Asynchronous and Event-Based Fusion Systems for Affect Recognition on
Naturalistic Data in Comparison to Conventional Approaches,
AffCom(9), No. 4, October 2018, pp. 410-423.
IEEE DOI
1812
Hidden Markov models, Emotion recognition,
Recurrent neural networks, Visualization, Heuristic algorithms,
deep learning
BibRef
Jain, N.[Neha],
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PRL(115), 2018, pp. 101-106.
Elsevier DOI
1812
Emotion recognition, Deep learning, Recurrent neural networks,
Convolutional Neural Networks, Hybrid CNN-RNN
BibRef
Jain, D.K.[Deepak Kumar],
Shamsolmoali, P.[Pourya],
Sehdev, P.[Paramjit],
Extended deep neural network for facial emotion recognition,
PRL(120), 2019, pp. 69-74.
Elsevier DOI
1904
Facial emotion recognition, Deep neural network, Fully convolution network
BibRef
Li, S.[Shan],
Deng, W.H.[Wei-Hong],
Blended Emotion in-the-Wild: Multi-label Facial Expression Recognition
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IJCV(127), No. 6-7, June 2019, pp. 884-906.
Springer DOI
1906
BibRef
Earlier:
Deep Emotion Transfer Network for Cross-database Facial Expression
Recognition,
ICPR18(3092-3099)
IEEE DOI
1812
Databases, Face recognition, Kernel, Training, Task analysis, Testing
BibRef
Zhang, W.,
He, X.,
Lu, W.,
Exploring Discriminative Representations for Image Emotion
Recognition With CNNs,
MultMed(22), No. 2, February 2020, pp. 515-523.
IEEE DOI
2001
Image emotion classification, discriminative representation,
emotional inference, deep learning, convolutional neural networks
BibRef
Tian, Y.,
Cheng, J.,
Li, Y.,
Wang, S.,
Secondary Information Aware Facial Expression Recognition,
SPLetters(26), No. 12, December 2019, pp. 1753-1757.
IEEE DOI
2001
emotion recognition, face recognition, image classification,
learning (artificial intelligence), SIFE-Net, FER datasets,
deep learning
BibRef
Naqvi, R.A.[Rizwan Ali],
Arsalan, M.[Muhammad],
Rehman, A.[Abdul],
Ur Rehman, A.[Ateeq],
Loh, W.K.[Woong-Kee],
Paul, A.[Anand],
Deep Learning-Based Drivers Emotion Classification System in Time
Series Data for Remote Applications,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link
2002
BibRef
Parthasarathy, S.[Srinivas],
Busso, C.[Carlos],
Predicting Emotionally Salient Regions Using Qualitative Agreement of
Deep Neural Network Regressors,
AffCom(12), No. 2, April 2021, pp. 402-416.
IEEE DOI
2106
Emotion recognition, Market research, Databases,
Affective computing, Speech recognition, Task analysis,
regressors of attribute-based descriptors
BibRef
Kollias, D.[Dimitrios],
Zafeiriou, S.P.[Stefanos P.],
Exploiting Multi-CNN Features in CNN-RNN Based Dimensional Emotion
Recognition on the OMG in-the-Wild Dataset,
AffCom(12), No. 3, July 2021, pp. 595-606.
IEEE DOI
2109
Feature extraction, Estimation, Databases, Computer architecture,
Visualization, Machine learning, Emotion recognition,
OMG-Emotion database and challenge
See also OMG-Emotion (One-Minute Gradual-Emotional Behavior).
BibRef
Al Chanti, D.[Dawood],
Caplier, A.[Alice],
Deep Learning for Spatio-Temporal Modeling of Dynamic Spontaneous
Emotions,
AffCom(12), No. 2, April 2021, pp. 363-376.
IEEE DOI
2106
Spatiotemporal phenomena, Visualization, Face recognition, Face,
Videos, Machine learning, Computational modeling, 3D-CNN, ConvLSTM,
spatiotemporal features
BibRef
Zhang, H.[Haimin],
Xu, M.[Min],
Weakly Supervised Emotion Intensity Prediction for Recognition of
Emotions in Images,
MultMed(23), 2021, pp. 2033-2044.
IEEE DOI
2107
Emotion recognition, Streaming media, Visualization,
Feature extraction, Neural networks, Semantics, Annotations,
deep neural networks
BibRef
Hariri, W.[Walid],
Farah, N.[Nadir],
Recognition of 3D emotional facial expression based on handcrafted
and deep feature combination,
PRL(148), 2021, pp. 84-91.
Elsevier DOI
2107
Facial emotion, CNN, Deep covariance, Manifold, Gaussian kernel
See also 3D face recognition using covariance based descriptors.
BibRef
Yang, J.Y.[Jing-Yuan],
Li, J.[Jie],
Wang, X.M.[Xiu-Mei],
Ding, Y.X.[Yu-Xuan],
Gao, X.B.[Xin-Bo],
Stimuli-Aware Visual Emotion Analysis,
IP(30), 2021, pp. 7432-7445.
IEEE DOI
2109
Feature extraction, Visualization, Psychology, Task analysis,
Image color analysis, Face recognition, Deep learning,
convolutional neural networks
BibRef
Oveneke, M.C.[Meshia Cédric],
Zhao, Y.[Yong],
Pei, E.[Ercheng],
Berenguer, A.D.[Abel Díaz],
Jiang, D.M.[Dong-Mei],
Sahli, H.[Hichem],
Leveraging the Deep Learning Paradigm for Continuous Affect
Estimation from Facial Expressions,
AffCom(13), No. 1, January 2022, pp. 426-439.
IEEE DOI
2203
Deep learning, Estimation, Face recognition, Face,
Feature extraction, Training, Kalman filters, Affect estimation,
extended kalman filtering
BibRef
Akhtar, M.S.[Md Shad],
Ghosal, D.[Deepanway],
Ekbal, A.[Asif],
Bhattacharyya, P.[Pushpak],
Kurohashi, S.[Sadao],
All-in-One: Emotion, Sentiment and Intensity Prediction Using a
Multi-Task Ensemble Framework,
AffCom(13), No. 1, January 2022, pp. 285-297.
IEEE DOI
2203
Task analysis, Hidden Markov models, Sentiment analysis,
Deep learning, Computational modeling, Predictive models, ensemble
BibRef
Avola, D.[Danilo],
Cinque, L.[Luigi],
Fagioli, A.[Alessio],
Foresti, G.L.[Gian Luca],
Massaroni, C.[Cristiano],
Deep Temporal Analysis for Non-Acted Body Affect Recognition,
AffCom(13), No. 3, July 2022, pp. 1366-1377.
IEEE DOI
2209
Feature extraction, Emotion recognition, Task analysis, Games, Pain,
Skeleton, Non-acted affective computing, body movement,
long short-term memory (LSTM)
BibRef
Dias, W.[William],
Andaló, F.[Fernanda],
Padilha, R.[Rafael],
Bertocco, G.[Gabriel],
Almeida, W.[Waldir],
Costa, P.[Paula],
Rocha, A.[Anderson],
Cross-dataset emotion recognition from facial expressions through
convolutional neural networks,
JVCIR(82), 2022, pp. 103395.
Elsevier DOI
2201
Emotion recognition, Facial analysis, Cross-dataset evaluation, Deep learning
BibRef
Barros, P.[Pablo],
Barakova, E.[Emilia],
Wermter, S.[Stefan],
Adapting the Interplay Between Personalized and Generalized Affect
Recognition Based on an Unsupervised Neural Framework,
AffCom(13), No. 3, July 2022, pp. 1349-1365.
IEEE DOI
2209
Emotion recognition, Adaptation models, Computational modeling,
Deep learning, Data models, Feature extraction, Convolution,
continual learning
BibRef
Song, P.P.[Pei-Pei],
Guo, D.[Dan],
Cheng, J.[Jun],
Wang, M.[Meng],
Contextual Attention Network for Emotional Video Captioning,
MultMed(25), 2023, pp. 1858-1867.
IEEE DOI
2306
Visualization, Feature extraction, Task analysis, Emotion recognition,
Convolutional neural networks, Semantics, attention
BibRef
Song, P.P.[Pei-Pei],
Guo, D.[Dan],
Yang, X.[Xun],
Tang, S.G.[Shen-Geng],
Wang, M.[Meng],
Emotional Video Captioning With Vision-Based Emotion Interpretation
Network,
IP(33), 2024, pp. 1122-1135.
IEEE DOI
2402
Visualization, Vocabulary, Psychology, Task analysis, Generators,
Emotion recognition, Wheels, Emotional video captioning,
emotion-fact coordinated optimization
BibRef
Zheng, J.H.[Jia-Hao],
Zhang, S.[Sen],
Wang, Z.[Zilu],
Wang, X.P.[Xiao-Ping],
Zeng, Z.G.[Zhi-Gang],
Multi-Channel Weight-Sharing Autoencoder Based on Cascade Multi-Head
Attention for Multimodal Emotion Recognition,
MultMed(25), 2023, pp. 2213-2225.
IEEE DOI
2306
Feature extraction, Emotion recognition, Data models, Training,
Task analysis, Decoding, Data mining,
multi-head attention mechanism
BibRef
Zhang, C.J.[Chun-Jiong],
Li, M.Y.[Ming-Yong],
Wu, D.[Di],
Federated Multidomain Learning With Graph Ensemble Autoencoder GMM
for Emotion Recognition,
ITS(24), No. 7, July 2023, pp. 7631-7641.
IEEE DOI
2307
Face recognition, Task analysis, Collaborative work, Data models,
Image recognition, Adaptation models, Training, domain adaptability
BibRef
Liu, D.J.[Dong-Jun],
Dai, W.C.[Wei-Chen],
Zhang, H.K.[Hang-Kui],
Jin, X.Y.[Xuan-Yu],
Cao, J.T.[Jian-Ting],
Kong, W.Z.[Wan-Zeng],
Brain-Machine Coupled Learning Method for Facial Emotion Recognition,
PAMI(45), No. 9, September 2023, pp. 10703-10717.
IEEE DOI
2309
BibRef
Sherly-Alphonse, A.,
Abinaya, S.,
Abirami, S.,
Alibaba and forty thieves algorithm and novel Prioritized Prewitt
Pattern(PPP)-based convolutional neural network (CNN) using
hyperspherically compressed weights for facial emotion recognition,
JVCIR(97), 2023, pp. 103948.
Elsevier DOI
2312
Optimization, CNN, Weights, Emotion, Hyperparameters
BibRef
Wu, Y.J.[Yu-Jin],
Daoudi, M.[Mohamed],
Amad, A.[Ali],
Transformer-Based Self-Supervised Multimodal Representation Learning
for Wearable Emotion Recognition,
AffCom(15), No. 1, January 2024, pp. 157-172.
IEEE DOI
2403
Emotion recognition, Task analysis, Physiology, Feature extraction,
Data models, Transformers, Self-supervised learning,
transformers
BibRef
Zell, A.[Adina],
Sumbul, G.[Gencer],
Demir, B.[Begüm],
Deep Metric Learning-Based Semi-Supervised Regression with Alternate
Learning,
ICIP22(2411-2415)
IEEE DOI
2211
Training, Emotion recognition, Parameter estimation, Codes,
Neural networks, Deep architecture, Semi-supervised regression, deep learning
BibRef
Jeong, E.[Euiseok],
Oh, G.[Geesung],
Lim, S.[Sejoon],
Multi-task Learning for Human Affect Prediction with Auditory-Visual
Synchronized Representation,
ABAW22(2437-2444)
IEEE DOI
2210
Deep learning, Visualization, Annotations, Predictive models,
Feature extraction, Multitasking, Data models
BibRef
Khan, A.S.[Ahmed Shehab],
Li, Z.Y.[Zhi-Yuan],
Cai, J.[Jie],
Tong, Y.[Yan],
Regional Attention Networks with Context-aware Fusion for Group
Emotion Recognition,
WACV21(1149-1158)
IEEE DOI
2106
Visualization, Emotion recognition, Sentiment analysis, Fuses,
Computational modeling, Pressing, Streaming media
BibRef
Peña, A.,
Morales, A.,
Serna, I.,
Fierrez, J.,
Lapedriza, A.,
Facial Expressions as a Vulnerability in Face Recognition,
ICIP21(2988-2992)
IEEE DOI
2201
Image recognition, Databases, Face recognition, Genetic expression,
Detectors, Security, Face Recognition, Expression
BibRef
Peña, A.[Alejandro],
Fierrez, J.[Julian],
Morales, A.[Aythami],
Lapedriza, A.[Agata],
Learning Emotional-Blinded Face Representations,
ICPR21(3566-3573)
IEEE DOI
2105
Emotion recognition, Privacy, Face recognition, Process control,
Regulation, Emotional responses, Task analysis
BibRef
Gund, M.[Manasi],
Bharadwaj, A.R.[Abhiram Ravi],
Nwogu, I.[Ifeoma],
Interpretable Emotion Classification Using Temporal Convolutional
Models,
ICPR21(6367-6374)
IEEE DOI
2105
Convolutional codes, Analytical models, Pain, Face recognition,
Neural networks, Predictive models, Encoding
BibRef
Aledhari, M.[Mohammed],
Razzak, R.[Rehma],
Parizi, R.M.[Reza M.],
Srivastava, G.[Gautam],
Deep Neural Networks for Detecting Real Emotions Using Biofeedback and
Voice,
HCAU20(302-309).
Springer DOI
2103
BibRef
Prawiro, H.,
Pan, T.Y.,
Hu, M.C.,
An Empirical Study of Emotion Recognition from Thermal Video Based on
Deep Neural Networks,
VCIP20(407-410)
IEEE DOI
2102
Emotion recognition, Solid modeling, Gray-scale, Convolution,
deep neural network
BibRef
Ozkan, S.,
Akar, G.B.,
Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake
Expression Prediction,
EmotionComp17(3094-3100)
IEEE DOI
1802
Computational modeling, Computer architecture, Data models, Face,
Feature extraction, Robustness, Visualization
BibRef
Moghadam, S.M.,
Seyyedsalehi, S.A.,
Nonlinear analysis of video images using deep recurrent
auto-associative neural networks for facial understanding,
IPRIA17(20-25)
IEEE DOI
1712
emotion recognition, face recognition, feature extraction,
image sequences, learning (artificial intelligence),
Nonlinear Video Analysis
BibRef
Kim, B.K.,
Dong, S.Y.,
Roh, J.,
Kim, G.,
Lee, S.Y.,
Fusing Aligned and Non-aligned Face Information for Automatic Affect
Recognition in the Wild: A Deep Learning Approach,
Affect16(1499-1508)
IEEE DOI
1612
BibRef
Khorrami, P.,
Le Paine, T.,
Brady, K.,
Dagli, C.,
Huang, T.S.,
How deep neural networks can improve emotion recognition on video
data,
ICIP16(619-623)
IEEE DOI
1610
Convolution
BibRef
Deng, W.,
Hu, J.,
Zhang, S.,
Guo, J.,
DeepEmo: Real-world facial expression analysis via deep learning,
VCIP15(1-4)
IEEE DOI
1605
Databases
BibRef
Chen, M.[Ming],
Zhang, L.[Lu],
Allebach, J.P.[Jan P.],
Learning deep features for image emotion classification,
ICIP15(4491-4495)
IEEE DOI
1512
Image emotion
BibRef
Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Emotion Recognition, Survey, General, Review, Datasets, Database .