Soleymani, M.[Mohammad],
Lichtenauer, J.,
Pun, T.[Thierry],
Pantic, M.[Maja],
A Multimodal Database for Affect Recognition and Implicit Tagging,
AffCom(3), No. 1, 2012, pp. 42-55.
IEEE DOI
1202
BibRef
Soleymani, M.[Mohammad],
Pantic, M.[Maja],
Pun, T.[Thierry],
Multimodal Emotion Recognition in Response to Videos,
AffCom(3), No. 2, 2012, pp. 211-223.
IEEE DOI
1208
BibRef
McKeown, G.,
Valstar, M.F.,
Cowie, R.,
Pantic, M.,
Schroder, M.,
The SEMAINE Database: Annotated Multimodal Records of Emotionally
Colored Conversations between a Person and a Limited Agent,
AffCom(3), No. 1, 2012, pp. 5-17.
IEEE DOI
1202
BibRef
Wagner, J.,
Andre, E.,
Lingenfelser, F.,
Kim, J.H.[Jong-Hwa],
Exploring Fusion Methods for Multimodal Emotion Recognition with
Missing Data,
AffCom(2), No. 4, 2011, pp. 206-218.
IEEE DOI
1202
BibRef
Lu, K.[Kun],
Zhang, X.[Xin],
Multimodal Affect Recognition Using Boltzmann Zippers,
IEICE(E96-D), No. 11, November 2013, pp. 2496-2499.
WWW Link.
1311
BibRef
Li, H.B.[Hui-Bin],
Ding, H.X.[Hua-Xiong],
Huang, D.[Di],
Wang, Y.H.[Yun-Hong],
Zhao, X.[Xi],
Morvan, J.M.[Jean-Marie],
Chen, L.M.[Li-Ming],
An efficient multimodal 2D + 3D feature-based approach to automatic
facial expression recognition,
CVIU(140), No. 1, 2015, pp. 83-92.
Elsevier DOI
1509
Facial expression recognition
BibRef
Zhen, Q.K.[Qing-Kai],
Huang, D.[Di],
Wang, Y.H.[Yun-Hong],
Chen, L.M.[Li-Ming],
Muscular Movement Model-Based Automatic 3D/4D Facial Expression
Recognition,
MultMed(18), No. 7, July 2016, pp. 1438-1450.
IEEE DOI
1608
BibRef
Earlier:
Muscular Movement Model Based Automatic 3D Facial Expression
Recognition,
MMMod15(I: 522-533).
Springer DOI
1501
emotion recognition
BibRef
Zhao, X.[Xi],
Dellandrea, E.[Emmanuel],
Chen, L.M.[Li-Ming],
Kakadiaris, I.A.,
Accurate Landmarking of Three-Dimensional Facial Data in the Presence
of Facial Expressions and Occlusions Using a Three-Dimensional
Statistical Facial Feature Model,
SMC-B(41), No. 5, October 2011, pp. 1417-1428.
IEEE DOI
1110
BibRef
Earlier: A1, A2, A3, Only:
A 3D Statistical Facial Feature Model and Its Application on Locating
Facial Landmarks,
ACIVS09(686-697).
Springer DOI
0909
See also unified probabilistic framework for automatic 3D facial expression analysis based on a Bayesian belief inference and statistical feature models, A.
BibRef
Zhao, X.[Xi],
Szeptycki, P.[Przemyslaw],
Dellandrea, E.[Emmanuel],
Chen, L.M.[Li-Ming],
Precise 2.5D facial landmarking via an analysis by synthesis approach,
WACV09(1-7).
IEEE DOI
0912
BibRef
Zhao, X.[Xi],
Huang, D.[Di],
Dellandrea, E.[Emmanuel],
Chen, L.M.[Li-Ming],
Automatic 3D Facial Expression Recognition Based on a Bayesian Belief
Net and a Statistical Facial Feature Model,
ICPR10(3724-3727).
IEEE DOI
1008
BibRef
Fu, H.Z.[Huan-Zhang],
Xiao, Z.Z.[Zhong-Zhe],
Dellandréa, E.[Emmanuel],
Dou, W.B.[Wei-Bei],
Chen, L.M.[Li-Ming],
Image Categorization Using ESFS:
A New Embedded Feature Selection Method Based on SFS,
ACIVS09(288-299).
Springer DOI
0909
Feature selection.
BibRef
Zhalehpour, S.[Sara],
Akhtar, Z.[Zahid],
Erdem, C.E.[Cigdem Eroglu],
Multimodal emotion recognition based on peak frame selection from video,
SIViP(10), No. 5, May 2016, pp. 827-834.
WWW Link.
1608
BibRef
Wen, H.W.[Hong-Wei],
Liu, Y.[Yue],
Rekik, I.[Islem],
Wang, S.P.[Sheng-Pei],
Chen, Z.Q.[Zhi-Qiang],
Zhang, J.S.[Ji-Shui],
Zhang, Y.[Yue],
Peng, Y.[Yun],
He, H.G.[Hui-Guang],
Multi-modal multiple kernel learning for accurate identification of
Tourette syndrome children,
PR(63), No. 1, 2017, pp. 601-611.
Elsevier DOI
1612
Tourette syndrome
BibRef
Tsalamlal, M.Y.,
Amorim, M.,
Martin, J.,
Ammi, M.,
Combining Facial Expression and Touch for Perceiving Emotional
Valence,
AffCom(9), No. 4, October 2018, pp. 437-449.
IEEE DOI
1812
Face recognition, Visualization, Haptic interfaces,
Emotion recognition, Human computer interaction,
multimodality
BibRef
Poria, S.[Soujanya],
Majumder, N.[Navonil],
Hazarika, D.[Devamanyu],
Cambria, E.[Erik],
Gelbukh, A.[Alexander],
Hussain, A.[Amir],
Multimodal Sentiment Analysis: Addressing Key Issues and Setting Up
the Baselines,
IEEE_Int_Sys(33), No. 6, November 2018, pp. 17-25.
IEEE DOI
1902
Role of speaker models, importance of different modalities, generalizability.
Sentiment analysis, Feature extraction, Visualization,
Emotion recognition, Affective computing,
Intelligent systems
BibRef
Lee, J.Y.[Ji-Young],
Kim, S.[Sunok],
Kim, S.R.[Seung-Ryong],
Sohn, K.H.[Kwang-Hoon],
Multi-Modal Recurrent Attention Networks for Facial Expression
Recognition,
IP(29), 2020, pp. 6977-6991.
IEEE DOI
2007
Face recognition, Image color analysis, Videos,
Emotion recognition, Benchmark testing, Databases, Task analysis,
attention mechanism
BibRef
Nguyen, D.[Dung],
Nguyen, K.[Kien],
Sridharan, S.[Sridha],
Dean, D.[David],
Fookes, C.[Clinton],
Deep spatio-temporal feature fusion with compact bilinear pooling for
multimodal emotion recognition,
CVIU(174), 2018, pp. 33-42.
Elsevier DOI
1812
BibRef
Nguyen, D.[Dung],
Nguyen, K.[Kien],
Sridharan, S.[Sridha],
Ghasemi, A.[Afsane],
Dean, D.[David],
Fookes, C.[Clinton],
Deep Spatio-Temporal Features for Multimodal Emotion Recognition,
WACV17(1215-1223)
IEEE DOI
1609
Convolution, Emotion recognition, Face, Feature extraction, Speech,
Speech recognition, Streaming, media
BibRef
Ghaleb, E.,
Popa, M.,
Asteriadis, S.,
Metric Learning-Based Multimodal Audio-Visual Emotion Recognition,
MultMedMag(27), No. 1, January 2020, pp. 37-48.
IEEE DOI
2004
Measurement, Emotion recognition, Visualization,
Support vector machines, Feature extraction, Task analysis,
Fisher vectors
BibRef
Chen, H.F.[Hai-Feng],
Jiang, D.M.[Dong-Mei],
Sahli, H.[Hichem],
Transformer Encoder With Multi-Modal Multi-Head Attention for
Continuous Affect Recognition,
MultMed(23), 2021, pp. 4171-4183.
IEEE DOI
2112
Emotion recognition, Context modeling, Feature extraction,
Correlation, Computational modeling, Visualization, Redundancy,
inter-modality interaction
BibRef
Zhang, K.[Ke],
Li, Y.Q.[Yuan-Qing],
Wang, J.Y.[Jing-Yu],
Wang, Z.[Zhen],
Li, X.L.[Xue-Long],
Feature Fusion for Multimodal Emotion Recognition Based on Deep
Canonical Correlation Analysis,
SPLetters(28), 2021, pp. 1898-1902.
IEEE DOI
2110
Feature extraction, Correlation, Emotion recognition, TV,
Visualization, Analytical models, Logic gates,
multimodal emotion recognition
BibRef
Tseng, S.Y.[Shao-Yen],
Narayanan, S.[Shrikanth],
Georgiou, P.[Panayiotis],
Multimodal Embeddings From Language Models for Emotion Recognition in
the Wild,
SPLetters(28), 2021, pp. 608-612.
IEEE DOI
2104
Acoustics, Task analysis, Feature extraction, Convolution,
Emotion recognition, Context modeling, Bit error rate
BibRef
Huynh, V.T.[Van Thong],
Yang, H.J.[Hyung-Jeong],
Lee, G.S.[Guee-Sang],
Kim, S.H.[Soo-Hyung],
End-to-End Learning for Multimodal Emotion Recognition in Video With
Adaptive Loss,
MultMedMag(28), No. 2, April 2021, pp. 59-66.
IEEE DOI
2107
Feature extraction, Convolution, Emotion recognition, Data mining,
Face recognition, Visualization, Training data, Affective Computing
BibRef
Nguyen, D.[Dung],
Nguyen, D.T.[Duc Thanh],
Zeng, R.[Rui],
Nguyen, T.T.[Thanh Thi],
Tran, S.N.[Son N.],
Nguyen, T.[Thin],
Sridharan, S.[Sridha],
Fookes, C.[Clinton],
Deep Auto-Encoders With Sequential Learning for Multimodal
Dimensional Emotion Recognition,
MultMed(24), 2022, pp. 1313-1324.
IEEE DOI
2204
Emotion recognition, Feature extraction, Long short term memory,
Visualization, Streaming media, Convolution, Auto-encoder,
multimodal emotion recognition
BibRef
Li, C.Q.[Chi-Qin],
Xie, L.[Lun],
Pan, H.[Hang],
Branch-Fusion-Net for Multi-Modal Continuous Dimensional Emotion
Recognition,
SPLetters(29), 2022, pp. 942-946.
IEEE DOI
2205
Emotion recognition, Feature extraction, Convolution, Fuses,
Convolutional neural networks, Data models, Context modeling,
feature fusion
BibRef
Gao, L.[Lei],
Guan, L.[Ling],
A Discriminative Vectorial Framework for Multi-Modal Feature
Representation,
MultMed(24), 2022, pp. 1503-1514.
IEEE DOI
2204
Semantics, Correlation, Task analysis, Emotion recognition,
Visualization, Transforms, Image recognition,
multi-modal hashing
BibRef
Yang, D.K.[Ding-Kang],
Huang, S.[Shuai],
Liu, Y.[Yang],
Zhang, L.H.[Li-Hua],
Contextual and Cross-Modal Interaction for Multi-Modal Speech Emotion
Recognition,
SPLetters(29), 2022, pp. 2093-2097.
IEEE DOI
2211
Transformers, Emotion recognition, Convolution, Acoustics,
Speech recognition, Stacking, Pipelines, Contextual interaction,
speech emotion recognition
BibRef
Shukla, A.[Abhinav],
Petridis, S.[Stavros],
Pantic, M.[Maja],
Does Visual Self-Supervision Improve Learning of Speech
Representations for Emotion Recognition?,
AffCom(14), No. 1, January 2023, pp. 406-420.
IEEE DOI
2303
Visualization, Task analysis, Speech recognition,
Emotion recognition, Training, Image reconstruction,
cross-modal self-supervision
BibRef
Hu, J.X.[Jia-Xiong],
Huang, Y.[Yun],
Hu, X.Z.[Xiao-Zhu],
Xu, Y.Q.[Ying-Qing],
The Acoustically Emotion-Aware Conversational Agent With Speech
Emotion Recognition and Empathetic Responses,
AffCom(14), No. 1, January 2023, pp. 17-30.
IEEE DOI
2303
Emotion recognition, Speech recognition, Databases,
Sentiment analysis, Games, Convolutional neural networks,
intelligent agents
BibRef
Candemir, C.[Cemre],
Gonul, A.S.[Ali Saffet],
Selver, M.A.[M. Alper],
Automatic Detection of Emotional Changes Induced by Social Support
Loss Using fMRI,
AffCom(14), No. 1, January 2023, pp. 706-717.
IEEE DOI
2303
Functional magnetic resonance imaging, Task analysis, Games,
Transient analysis, Signal to noise ratio, Shape,
emotional change (EC)
BibRef
Ping, H.Q.[Huan-Qin],
Zhang, D.[Dong],
Zhu, S.[Suyang],
Li, J.H.[Jun-Hui],
Zhou, G.D.[Guo-Dong],
A Benchmark for Hierarchical Emotion Cause Extraction in Spoken
Dialogues,
SPLetters(30), 2023, pp. 558-562.
IEEE DOI
2305
Task analysis, Feature extraction, Emotion recognition,
Bit error rate, Oral communication, Data mining, Preforms, spoken dialogues
BibRef
Bhattacharya, P.[Prasanta],
Gupta, R.K.[Raj Kumar],
Yang, Y.P.[Yin-Ping],
Exploring the Contextual Factors Affecting Multimodal Emotion
Recognition in Videos,
AffCom(14), No. 2, April 2023, pp. 1547-1557.
IEEE DOI
2306
Emotion recognition, Videos, Visualization, Feature extraction,
Physiology, High performance computing, Distance measurement,
technology & devices for affective computing
BibRef
Li, W.[Wei],
Finding Needles in a Haystack: Recognizing Emotions Just From Your
Heart,
AffCom(14), No. 2, April 2023, pp. 1488-1505.
IEEE DOI
2306
Electrocardiography, Feature extraction, Heart,
Emotion recognition, Heart rate variability, Physiology,
finding needles in a haystack
BibRef
Chang, C.M.[Chun-Min],
Chao, G.Y.[Gao-Yi],
Lee, C.C.[Chi-Chun],
Enforcing Semantic Consistency for Cross Corpus Emotion Prediction
Using Adversarial Discrepancy Learning in Emotion,
AffCom(14), No. 2, April 2023, pp. 1098-1109.
IEEE DOI
2306
Databases, Semantics, Emotion recognition, Acoustic distortion,
Training, Nonlinear distortion, Correlation, domain adaptation
BibRef
Benssassi, E.M.[Esma Mansouri],
Ye, J.[Juan],
Investigating Multisensory Integration in Emotion Recognition Through
Bio-Inspired Computational Models,
AffCom(14), No. 2, April 2023, pp. 906-918.
IEEE DOI
2306
Feature extraction, Emotion recognition, Visualization,
Brain modeling, Support vector machines,
graph neural network
BibRef
Dong, K.[Ke],
Peng, H.[Hao],
Che, J.[Jie],
Dynamic-static Cross Attentional Feature Fusion Method for Speech
Emotion Recognition,
MMMod23(II: 350-361).
Springer DOI
2304
BibRef
Parameshwara, R.[Ravikiran],
Radwan, I.[Ibrahim],
Subramanian, R.[Ramanathan],
Goecke, R.[Roland],
Examining Subject-Dependent and Subject-Independent Human Affect
Inference from Limited Video Data,
FG23(1-6)
IEEE DOI
2303
Training, Databases, Annotations, Face recognition,
Memory architecture, Estimation, Gesture recognition
BibRef
Mathur, L.[Leena],
Adolphs, R.[Ralph],
Mataric, M.J.[Maja J.],
Towards Intercultural Affect Recognition: Audio-Visual Affect
Recognition in the Wild Across Six Cultures,
FG23(1-6)
IEEE DOI
2303
Training, Visualization, Systematics, Face recognition,
Computational modeling, Gesture recognition, Robustness
BibRef
Gomaa, A.[Ahmed],
Maier, A.[Andreas],
Kosti, R.[Ronak],
Supervised Contrastive Learning for Robust and Efficient Multi-modal
Emotion and Sentiment Analysis,
ICPR22(2423-2429)
IEEE DOI
2212
Training, Sentiment analysis, Computational modeling,
Predictive models, Transformers, Robustness
BibRef
Liu, H.Y.[Hai-Yang],
Zhu, Z.[Zihao],
Iwamoto, N.[Naoya],
Peng, Y.C.[Yi-Chen],
Li, Z.Q.[Zheng-Qing],
Zhou, Y.[You],
Bozkurt, E.[Elif],
Zheng, B.[Bo],
BEAT: A Large-Scale Semantic and Emotional Multi-modal Dataset for
Conversational Gestures Synthesis,
ECCV22(VII:612-630).
Springer DOI
2211
Dataset, Emotions.
BibRef
Chudasama, V.[Vishal],
Kar, P.[Purbayan],
Gudmalwar, A.[Ashish],
Shah, N.[Nirmesh],
Wasnik, P.[Pankaj],
Onoe, N.[Naoyuki],
M2FNet: Multi-modal Fusion Network for Emotion Recognition in
Conversation,
MULA22(4651-4660)
IEEE DOI
2210
Human computer interaction, Emotion recognition, Visualization,
Adaptation models, Benchmark testing, Feature extraction, Robustness
BibRef
Patania, S.[Sabrina],
d'Amelio, A.[Alessandro],
Lanzarotti, R.[Raffaella],
Exploring Fusion Strategies in Deep Multimodal Affect Prediction,
CIAP22(II:730-741).
Springer DOI
2205
BibRef
Lusquino Filho, L.A.D.,
Oliveira, L.F.R.,
Carneiro, H.C.C.,
Guarisa, G.P.,
Filho, A.L.,
França, F.M.G.,
Lima, P.M.V.,
A weightless regression system for predicting multi-modal empathy,
FG20(657-661)
IEEE DOI
2102
Training, Random access memory,
Mel frequency cepstral coefficient, Predictive models,
regression wisard
BibRef
Shao, J.,
Zhu, J.,
Wei, Y.,
Feng, Y.,
Zhao, X.,
Emotion Recognition by Edge-Weighted Hypergraph Neural Network,
ICIP19(2144-2148)
IEEE DOI
1910
Emotion recognition, edge-weighted hypergraph neural network, multi-modality
BibRef
Guo, J.,
Zhou, S.,
Wu, J.,
Wan, J.,
Zhu, X.,
Lei, Z.,
Li, S.Z.,
Multi-modality Network with Visual and Geometrical Information for
Micro Emotion Recognition,
FG17(814-819)
IEEE DOI
1707
Computer architecture, Emotion recognition, Face, Face recognition,
Feature extraction, Geometry, Visualization
BibRef
Wan, J.,
Escalera, S.,
Anbarjafari, G.,
Escalante, H.J.,
Baro, X.,
Guyon, I.,
Madadi, M.,
Allik, J.,
Gorbova, J.,
Lin, C.,
Xie, Y.,
Results and Analysis of ChaLearn LAP Multi-modal Isolated and
Continuous Gesture Recognition, and Real Versus Fake Expressed
Emotions Challenges,
EmotionComp17(3189-3197)
IEEE DOI
1802
Emotion recognition, Feature extraction, Gesture recognition,
Skeleton, Spatiotemporal phenomena, Training
BibRef
Ranganathan, H.,
Chakraborty, S.,
Panchanathan, S.,
Multimodal emotion recognition using deep learning architectures,
WACV16(1-9)
IEEE DOI
1606
Databases
BibRef
Wei, H.L.[Hao-Lin],
Monaghan, D.S.[David S.],
O'Connor, N.E.[Noel E.],
Scanlon, P.[Patricia],
A New Multi-modal Dataset for Human Affect Analysis,
HBU14(42-51).
Springer DOI
1411
Dataset, Human Affect.
BibRef
Chen, S.Z.[Shi-Zhi],
Tian, Y.L.[Ying-Li],
Margin-constrained multiple kernel learning based multi-modal fusion
for affect recognition,
FG13(1-7)
IEEE DOI
1309
face recognition
BibRef
Gajsek, R.[Rok],
Štruc, V.[Vitomir],
Mihelic, F.[France],
Multi-modal Emotion Recognition Using Canonical Correlations and
Acoustic Features,
ICPR10(4133-4136).
IEEE DOI
1008
BibRef
Escalera, S.[Sergio],
Puertas, E.[Eloi],
Radeva, P.I.[Petia I.],
Pujol, O.[Oriol],
Multi-modal laughter recognition in video conversations,
CVPR4HB09(110-115).
IEEE DOI
0906
BibRef
Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Emotion Recognition, from Other Than Faces .