Facial emotion recognition using multi-modal information

Is there universal recognition of emotion from facial expression? A review of the cross-cultural studies.

Russell, J. A. (1994) · Psychological Bulletin · doi:10.1037/0033-2909.115.1.102 · Cited by 1,640

Strong evidence for universals in facial expressions: A reply to Russell's mistaken critique.

Ekman, P. (1994) · Psychological Bulletin · doi:10.1037//0033-2909.115.2.268 · Cited by 1,130

Facial expression recognition using a dynamic model and motion energy

Essa, I. A., & Pentland, A. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466916 · Cited by 253

The Relative Dominance of Different Facial Expressions of Emotion under Conditions of Perceptual Ambiguity

Coren, S., & Russell, J. A. (1992) · Cognition & Emotion · doi:10.1080/02699939208409690 · Cited by 51

Emotion Enhanced Face to Face Meetings Using the Concept of Virtual Space Teleconferencing

Silva, L. C. D., Miyasato, T., & Kishino, F. (1996) · IEICE Transactions on Information and Systems · Cited by 11

Facial Expression Recognition from Image Sequence Using Hidden Markov Model

Sakaguchi, T., Ohya, J., & Kishino, F. (1995) · The Journal of The Institute of Image Information and Television Engineers · Cited by 11

Sensing of Feeling and "Kansei". Facial Expression Recognition from Image Sequence Using Hidden Markov Model.

Sakaguchi, T., Ohya, J., & Kishino, F. (1995) · The Journal of The Institute of Image Information and Television Engineers · doi:10.3169/itej1978.49.1060 · Cited by 8

Emotion enhanced multimedia meetings using the concept of virtual space teleconferencing

Silva, L. C. D., Miyasato, T., & Kishino, F. (2002) · IEEE International Conference on Multimedia Computing and Systems (ICMCS) · doi:10.1109/mmcs.1996.534951 · Cited by 6

Degree of Human Perception of Facial Emotions Based on Audio and Video Information

Silva, L. C. D., & Miyasato, T. (1996) · 電子情報通信学会技術研究報告. IE, 画像工学 · Cited by 2

IEMOCAP: Interactive emotional dyadic motion capture database

Busso, C., Bulut, M., Lee, C.-C., Kazemzadeh, A., Mower, E., Kim, S., Chang, J. N., Lee, S., & Narayanan, S. S. (2008) · Language Resources and Evaluation · doi:10.1007/s10579-008-9076-6 · Cited by 3,869

Toward machine emotional intelligence: analysis of affective physiological state

Picard, R. W., Vyzas, E., & Healey, J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.954607 · Cited by 2,334

Survey on speech emotion recognition: Features, classification schemes, and databases

Ayadi, M. M. H. E., Kamel, M. S., & Karray, F. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.09.020 · Cited by 2,139

Automatic analysis of facial expressions: the state of the art

Pantie, M., & Rothkrantz, L. J. M. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.895976 · Cited by 1,778

Efficient Low-rank Multimodal Fusion With Modality-Specific Factors

Liu, Z., Shen, Y., Lakshminarasimhan, V. B., Liang, P. P., Bagher Zadeh, A., & Morency, L.-P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1209 · Cited by 1,170

CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset

Cao, H., Cooper, D. G., Keutmann, M. K., Gur, R. C., Nenkova, A., & Verma, R. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2336244 · Cited by 954

Context-Dependent Sentiment Analysis in User-Generated Videos

Poria, S., Cambria, E., Hazarika, D., Majumder, N., Zadeh, A., & Morency, L.-P. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1081 · Cited by 939

Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

Gravina, R., Alinia, P., Ghasemzadeh, H., & Fortino, G. (2017) · Information Fusion · doi:10.1016/j.inffus.2016.09.005 · Cited by 899

Analysis of emotion recognition using facial expressions, speech and multimodal information

Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, C. M., Kazemzadeh, A., Lee, S., Neumann, U., & Narayanan, S. (2004) · Proceedings of the 6th international conference on Multimodal interfaces · doi:10.1145/1027933.1027968 · Cited by 840

Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions

Gandhi, A., Adhvaryu, K., Poria, S., Cambria, E., & Hussain, A. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.09.025 · Cited by 783

An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion

Kort, B. W., Reilly, R., & Picard, R. W. (2002) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2001.943850 · Cited by 781

Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis

Poria, S., Chaturvedi, I., Cambria, E., & Hussain, A. (2016) · Proceedings · doi:10.1109/icdm.2016.0055 · Cited by 624

Facial expression recognition from video sequences: temporal and static modeling

Cohen, I., Sebe, N., Garg, A., Chen, L. S., & Huang, T. S. (2003) · Computer Vision and Image Understanding · doi:10.1016/s1077-3142(03)00081-x · Cited by 874

Toward an affect-sensitive multimodal human-computer interaction

Pantic, M., & Rothkrantz, L. J. M. (2003) · Proceedings of the IEEE · doi:10.1109/jproc.2003.817122 · Cited by 791

Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications

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

Affective Learning — A Manifesto

Picard, R. W., Papert, S. A., Bender, W. R., Blumberg, B. M., Breazeal, C., Cavallo, D. P., Machover, T., Resnick, M., Roy, D. K., & Strohecker, C. (2004) · BT Technology Journal · doi:10.1023/b:bttj.0000047603.37042.33 · Cited by 518

Multimodal sentiment analysis using hierarchical fusion with context modeling

Majumder, N., Hazarika, D., Gelbukh, A. F., Cambria, E., & Poria, S. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.07.041 · Cited by 392

Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach

Katsis, C. D., Katertsidis, N. S., Ganiatsas, G., & Fotiadis, D. I. (2008) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2008.918624 · Cited by 332

State of the art of smart homes

Silva, L. C. D., Morikawa, C., & Petra, I. (2012) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2012.05.002 · Cited by 298

Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis

Kessous, L., Castellano, G., & Caridakis, G. (2010) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-009-0025-5 · Cited by 256

Multimodal Sentiment Analysis: Addressing Key Issues and Setting Up the Baselines

Poria, S., Majumder, N., Hazarika, D., Cambria, E., Gelbukh, A. F., & Hussain, A. (2018) · IEEE Intelligent Systems · doi:10.1109/mis.2018.2882362 · Cited by 264

Untitled work

(2013) · Cited by 236

Automated identification of media bias in news articles: an interdisciplinary literature review

Hamborg, F., Donnay, K., & Gipp, B. (2018) · International Journal on Digital Libraries · doi:10.1007/s00799-018-0261-y · Cited by 259

Recognizing Human Emotional State From Audiovisual Signals

Wang, Y., & Guan, L. (2008) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2008.921734 · Cited by 213

Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis

Poria, S., Peng, H., Hussain, A., Howard, N., & Cambria, E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.09.117 · Cited by 214

Gesture-Based Affective Computing on Motion Capture Data

Kapur, A., Kapur, A., Virji‐Babul, N., Tzanetakis, G., & Driessen, P. F. (2005) · Lecture notes in computer science · doi:10.1007/11573548_1 · Cited by 207

Recognizing Human Emotional State From Audiovisual Signals*

Wang, Y., & Guan, L. (2008) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2008.927665 · Cited by 202

Multimodal human emotion/expression recognition

Chen, L. S., Huang, T. S., Miyasato, T., & Nakatsu, R. (2002) · Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.1998.670976 · Cited by 183

Fear-type emotion recognition for future audio-based surveillance systems

Clavel, C., Vasilescu, I., Devillers, L., Richard, G., & Ehrette, T. (2008) · Speech Communication · doi:10.1016/j.specom.2008.03.012 · Cited by 180

Emotion Recognition from Facial Expressions using Multilevel HMM

Cohen, I. L., Garg, A., & Huang, T. S. (2000) · Cited by 169

A survey of speech emotion recognition in natural environment

Fahad, M. S., Ranjan, A., Yadav, J., & Deepak, A. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102951 · Cited by 167

Is there an advantage for recognizing multi-modal emotional stimuli?

Paulmann, S., & Pell, M. D. (2011) · Motivation and Emotion · doi:10.1007/s11031-011-9206-0 · Cited by 157

Computational approaches for emotion detection in text

Binali, H., Wu, C., & Potdar, V. M. (2010) · IEEE International Conference on Digital Ecosystems and Technologies · doi:10.1109/dest.2010.5610650 · Cited by 149

“How do you know that I don’t understand?” A look at the future of intelligent tutoring systems

Sarrafzadeh, A., Alexander, S., Dadgostar, F., Fan, C., & Bigdeli, A. (2007) · Computers in Human Behavior · doi:10.1016/j.chb.2007.07.008 · Cited by 147

Emotion recognition using a Cauchy Naive Bayes classifier

Sebe, N., Lew, M. S., Cohen, I., Garg, A., & Huang, T. S. (2003) · doi:10.1109/icpr.2002.1044578 · Cited by 140

Audiovisual Information Fusion in Human–Computer Interfaces and Intelligent Environments: A Survey

Shivappa, S. T., Trivedi, M. M., & Rao, B. D. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2057231 · Cited by 129

Interpreting ambiguous emotional expressions

Mower, E., Metallinou, A., Lee, C.-C., Kazemzadeh, A., Busso, C., Lee, S., & Narayanan, S. S. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349500 · Cited by 127

Multiple kernel learning for emotion recognition in the wild

Sikka, K., Dykstra, K., Sathyanarayana, S., Littlewort, G., & Bartlett, M. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531741 · Cited by 119

A Multimodal Emotion Detection System during Human–Robot Interaction

Alonso-Martín, F., Malfáz, M., Sequeira, J. S., Gorostiza, J. F., & Salichs, M. A. (2013) · Sensors · doi:10.3390/s131115549 · Cited by 117

Evaluating affective interactions: Alternatives to asking what users feel

Picard, R. W., & Daily, S. B. (2005) · Cited by 107

A neural network approach for human emotion recognition in speech

Bhatti, M. W., Wang, Y., & Guan, L. (2004) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2004.1329238 · Cited by 109

Towards a Learning Companion that Recognizes Affect

Kapoor, A., Mota, S., & Picard, R. W. (2001) · Cited by 107

Classifying pretended and evoked facial expressions of positive and negative affective states using infrared measurement of skin temperature

Khan, M. M., Ward, R. D., & Ingleby, M. (2009) · ACM Transactions on Applied Perception · doi:10.1145/1462055.1462061 · Cited by 106

Careful what you share in six seconds

Rafiq, R. I., Hosseinmardi, H., Han, R. Y., Lv, Q., Mishra, S., & Mattson, S. A. (2015) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2808797.2809381 · Cited by 92

The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements

Stappen, L., Baird, A., Schumann, L., & Schuller, B. W. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3097002 · Cited by 92

Facial expression recognition from video sequences

Cohen, I., Sebe, N., Garg, A., Lew, M. S., & Huang, T. S. (2003) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2002.1035527 · Cited by 90

Emotion Detection from Speech Signals using Voting Mechanism on Classified Frames

Zamil, A. A. A., Hasan, S., Baki, S. M. J., Adam, J. M., & Zaman, I. (2019) · International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) · doi:10.1109/icrest.2019.8644168 · Cited by 87

Agent based affective tutoring systems: A pilot study

Mao, X., & Li, Z. (2010) · Computers & Education · doi:10.1016/j.compedu.2010.01.005 · Cited by 83

Efficient Low-rank Multimodal Fusion with Modality-Specific FactorsPreprint

Liu, Z., Shen, Y., Lakshminarasimhan, V., Liang, P. P., Zadeh, A., & Morency, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.00064 · Cited by 80

Recognising Emotions from Keyboard Stroke Pattern

Khanna, P., & Sasikumar, M. (2010) · International Journal of Computer Applications · doi:10.5120/1614-2170 · Cited by 83

Approaches, Applications, and Challenges in Physiological Emotion Recognition—A Tutorial Overview

Can, Y. S., Mahesh, B., & André, E. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3286445 · Cited by 81

An integrated telemedicine platform for the assessment of affective physiological states

Katsis, C. D., Ganiatsas, G., & Fotiadis, D. I. (2006) · Diagnostic Pathology · doi:10.1186/1746-1596-1-16 · Cited by 66

Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm

Saha, S., Ghosh, M., Ghosh, S., Sen, S., Singh, P. K., Geem, Z. W., & Sarkar, R. (2020) · Applied Sciences · doi:10.3390/app10082816 · Cited by 63

WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art

Mohammad, S. M., & Kiritchenko, S. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/33oybkwsfa8r · Cited by 63

PRATIT: a CNN-based emotion recognition system using histogram equalization and data augmentation

Mungra, D., Agrawal, A., Sharma, P., Tanwar, S., & Obaidat, M. S. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08397-0 · Cited by 62

HEU Emotion: a large-scale database for multimodal emotion recognition in the wild

Chen, J., Wang, C., Wang, K., Yin, C., Zhao, C., Xu, T., Zhang, X., Huang, Z., Liu, M., & Yang, T. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05616-w · Cited by 56

External Representation of Learning Process and Domain Knowledge: Affective State as a Determinate of its Structure and Function

Kort, B. W., Reilly, R. B., & Picard, R. W. (2001) · Cited by 56

Multimodal Emotion Recognition Based on Cascaded Multichannel and Hierarchical Fusion

Liu, X., Xu, Z., & Huang, K. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/9645611 · Cited by 55

Recognizing Human Emotion from Audiovisual Informaiton

Wang, Y., & Guan, L. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2005.1415607 · Cited by 55

Emotion recognition from audiovisual information

Chen, L. S., Tao, H., Huang, T. S., Miyasato, T., & Nakatsu, R. (2002) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.1998.738917 · Cited by 54

Robust Audiovisual Emotion Recognition: Aligning Modalities, Capturing Temporal Information, and Handling Missing Features

Goncalves, L., & Busso, C. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3216993 · Cited by 55

Automatic emotion detection model from facial expression

Dagar, D., Hudait, A., Tripathy, H. K., & Das, M. N. (2016) · IEEE International Conference on Advanced Communications, Control and Computing Technologies · doi:10.1109/icaccct.2016.7831605 · Cited by 52

The Emotional, Cognitive, Physiological, and Performance Effects of Variable Time Delay in Robotic Teleoperation

Yang, E., & Dorneich, M. C. (2017) · International Journal of Social Robotics · doi:10.1007/s12369-017-0407-x · Cited by 52

Comparative Analytical Survey on Cognitive Agents with Emotional Intelligence

Zall, R., & Kangavari, M. R. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10007-5 · Cited by 56

A comprehensive survey on deep learning-based approaches for multimodal sentiment analysis

Ghorbanali, A., & Sohrabi, M. K. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10555-8 · Cited by 54

Real-time facial feature extraction and emotion recognition

Silva, L. C. D., & Hui, S. C. (2004) · International Conference on Information, Communications & Signal Processing (ICICS) · doi:10.1109/icics.2003.1292676 · Cited by 51

Emotional Prosody Processing in Schizophrenic Patients: A Selective Review and Meta-Analysis

Lin, Y., Ding, H., & Zhang, Y. (2018) · Journal of Clinical Medicine · doi:10.3390/jcm7100363 · Cited by 50

A Novel Exploitative and Explorative GWO-SVM Algorithm for Smart Emotion Recognition

Yan, X., Lin, Z., Lin, Z., & Vucetic, B. S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3235356 · Cited by 47

Machines Outperform Laypersons in Recognizing Emotions Elicited by Autobiographical Recollection

Janssen, J. H., Tacken, P., de Vries, G.-J., van den Broek, E. L., Westerink, J. H. D. M., Haselager, P., & IJsselsteijn, W. A. (2013) · Human-Computer Interaction · doi:10.1080/07370024.2012.755421 · Cited by 50

Facial Expression Recognition: A Fully Integrated Approach

Valenti, R., Sebe, N., & Gevers, T. (2007) · doi:10.1109/iciapw.2007.25 · Cited by 44

On assisting a visual-facial affect recognition system with keyboard-stroke pattern information

Σταθοπούλου, Ι., Alepis, E., Tsihrintzis, G. A., & Virvou, M. K. (2009) · Knowledge-Based Systems · doi:10.1016/j.knosys.2009.11.007 · Cited by 43

The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention

Jha, S., Marzban, M. F., Hu, T., Mahmoud, M. H., Al‐Dhahir, N., & Busso, C. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3095462 · Cited by 46

Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online ContentPreprint

Asghar, M. Z., Subhan, F., Imran, M., Kundi, F. M., Shamshirband, S., Mosavi, A., Csiba, P., & Várkonyi-Kóczy, A. R. (2019) · Preprints.org · doi:10.20944/preprints201908.0019.v1 · Cited by 43

Towards Improving Visual-Facial Emotion Recognition through Use of Complementary Keyboard-Stroke Pattern Information

Tsihrintzis, G. A., Virvou, M. K., Alepis, E., & Σταθοπούλου, Ι. (2008) · International Conference on Information Technology: New Generations · doi:10.1109/itng.2008.152 · Cited by 40

Multimodal information fusion for selected multimedia applications

Guan, L., Wang, Y., Zhang, R., Tie, Y., Bulzacki, A., & Ibrahim, M. (2010) · International Journal of Multimedia Intelligence and Security · doi:10.1504/ijmis.2010.035969 · Cited by 34

The Design and Analysis of a Real-Time, Continuous Arousal Monitor

Grundlehner, B., Brown, L., Penders, J., & Gyselinckx, B. (2009) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2009.21 · Cited by 39

Multi-Attention Multimodal Sentiment Analysis

Kim, T., & Lee, B. (2020) · International Conference on Multimedia Retrieval · doi:10.1145/3372278.3390698 · Cited by 39

Robust Computer Vision: Theory and Applications

Sebe, N., & Lew, M. S. (2003) · Cited by 38

RAMAS: Russian Multimodal Corpus of Dyadic Interaction for Affective Computing

Perepelkina, O. S., Kazimirova, E., & Konstantinova, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99579-3_52 · Cited by 37

Using physiological signal analysis to design affective VR games

Li, Y., Elmaghraby, A. S., El-Baz, A. S., & Sokhadze, E. M. (2015) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2015.7394401 · Cited by 37

Survey on Feature Extraction and Applications of Biosignals

Supratak, A., Wu, C., Dong, H., Sun, K., & Guo, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-50478-0_8 · Cited by 37

Decision-Level Fusion Method for Emotion Recognition using Multimodal Emotion Recognition Information

Song, K.-S., Nho, Y., Seo, J.-H., & Kwon, D. (2018) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/urai.2018.8441795 · Cited by 35

Modality-based Factorization for Multimodal Fusion

Barezi, E. J., & Fung, P. (2019) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w19-4331 · Cited by 39

Audio-lingual and Visual-facial Emotion Recognition: Towards a Bi-modal Interaction System

Alepis, E., Σταθοπούλου, Ι., Virvou, M. K., Tsihrintzis, G. A., & Kabassi, K. (2010) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2010.111 · Cited by 33

Classification of stress of automobile drivers using Radial Basis Function Kernel Support Vector Machine

Soman, K., Sathiya, A., & Suganthi, N. (2014) · International Conference on Information Communication and Embedded Systems · doi:10.1109/icices.2014.7034000 · Cited by 33

Face mask design to mitigate facial expression occlusion

Genç, Ç., Colley, A., Löchtefeld, M., & Häkkilä, J. (2020) · International Symposium on Wearable Computers · doi:10.1145/3410531.3414303 · Cited by 33

Harnessing emotions for depression detection

Prabhu, S. M., Mittal, H., Varagani, R., Jha, S., & Singh, S. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-01020-9 · Cited by 39

Emotion Classification from Speech and Text in Videos Using a Multimodal Approach

Caschera, M. C., Grifoni, P., & Ferri, F. (2022) · Multimodal Technologies and Interaction · doi:10.3390/mti6040028 · Cited by 34

Experience has a limited effect on humans’ ability to predict the outcome of social interactions in children, dogs and macaques

Donnier, S., Kovács, G., Oña, L. S., Bräuer, J., & Amici, F. (2020) · Scientific Reports · doi:10.1038/s41598-020-78275-5 · Cited by 32

The Effect of Time Delay on Emotion, Arousal, and Satisfaction in Human-Robot Interaction

Yang, E., & Dorneich, M. C. (2015) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931215591094 · Cited by 33

Affective computation on EEG correlates of emotion from musical and vocal stimuli

Khosrowabadi, R., Wahab, A., Ang, K. K., & Baniasad, M. H. (2009) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2009.5178748 · Cited by 31

Building a three-level multimodal emotion recognition framework

Garcia-Garcia, J. M., Lozano, M. D., Penichet, V. M. R., & Law, E. L. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13254-8 · Cited by 32

"Emotions That Influence Purchase Decisions And Their Electronic Processing "

Consoli, D. (2009) · Annales Universitatis Apulensis Series Oeconomica · doi:10.29302/oeconomica.2009.11.2.45 · Cited by 30

Unobtrusive Multimodal Emotion Detection in Adaptive Interfaces: Speech and Facial Expressions

Truong, K. P., van Leeuwen, D. A., & Neerincx, M. A. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73216-7_40 · Cited by 30

Speech emotion classification using combined neurogram and INTERSPEECH 2010 paralinguistic challenge features

Jassim, W. A., Paramesran, R., & Harte, N. (2017) · IET Signal Processing · doi:10.1049/iet-spr.2016.0336 · Cited by 30

A New Information Fusion Method for Bimodal Robotic Emotion Recognition

Han, M.-J., Hsu, J.-H., Song, K., & Chang, F. (2008) · Journal of Computers · doi:10.4304/jcp.3.7.39-47 · Cited by 29

Analysis and detection of labeled cyberbullying instances in Vine, a video-based social network

Rafiq, R. I., Hosseinmardi, H., Mattson, S. A., Han, R. Y., Lv, Q., & Mishra, S. (2016) · Social Network Analysis and Mining · doi:10.1007/s13278-016-0398-x · Cited by 29

Emotion recognition based on a novel triangular facial feature extraction method

Huang, K., Huang, S., & Kuo, Y.-H. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596374 · Cited by 28

Step-wise emotion recognition using concatenated-HMM

Özkan, D., Scherer, S. J., & Morency, L. (2012) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2388676.2388780 · Cited by 27

EEG-based emotion recognition using an improved radial basis function neural network

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Multimodal human emotion/expression recognition

Chen, L. S., Huang, T. S., Miyasato, T., & Nakatsu, R. (2002) · Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.1998.670976 · Cited by 183

Multimodal Emotion Recognition System Using Facial Expressions and Speech Analysis

Sachin, G., & Nithyanandh, S. (2026) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2612514

Combining Empirical Studies of Audio-Lingual and Visual-Facial Modalities for Emotion Recognition

Virvou, M. K., Tsihrintzis, G. A., Alepis, E., Σταθοπούλου, Ι., & Kabassi, K. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74827-4_141 · Cited by 13

Audio-Visual Emotion Recognition Using Gaussian Mixture Models for Face and Voice

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Analysis of emotion recognition using facial expressions, speech and multimodal information

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