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Facial emotion recognition using multi-modal information
Abstract
Facial emotion recognition will become vitally important in future multi-cultural visual communication systems, for emotion translation between cultures, which may be considered analogous to speech translation. However so far the recognition of facial emotions is mainly addressed by computer vision researchers, based on facial display. Also detection of vocal expressions of emotions can be found in research work done by acoustic researchers. Most of these research paradigms are devoted purely to visual or purely to auditory human emotion detection. However we found that it is very interesting to consider both these auditory and visual information together, for processing, since we hope this kind of multi-modal information processing will become a datum of information processing in future multimedia era. By several intensive subjective evaluation studies we found that human beings recognise anger, happiness, surprise and dislike by their visual appearance, compared to voice only detection. When the audio track of each emotion clip is dubbed with a different type of auditory emotional expression, still anger, happiness and surprise were video dominant. However the dislike emotion gave mixed responses to different speakers. In both studies we found that sadness and fear emotions were audio dominant. As a conclusion, we propose a method of facial emotion detection by using a hybrid approach, which uses multi-modal information for facial emotion recognition.
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“The method proposed by Cohn et al. deals neither with image sequences containing several facial actions in a row, nor with inaccurate facial data, nor with facial action intensity (yet the concept of the method makes it possible).”
From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [10]
In the citing paper
“The accuracy of the classification was 92 percent for the eyebrow region, 88 percent for the eye region and 83 percent for the nose/ mouth region.”
From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [10]
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Cites
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
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
Self-citationFacial 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
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
Self-citationDegree 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
Self-citation
Flagged first, then by citations
Research works printed but not linked: 6
- fridlund, 1994, Human facial expression An evalutionary view
- ekman, 1976, Pictures of Facial Effect
- 10.1037/0033-2909.115.2.268 · DOI · Paper page
- mase, 1991, Recognition of facial expressions from optiacl flow, IEICE Transactions, e74, 3474
- kobayashi, 1995, Monitoring of facial expressions, JSICE Journal of the Society of Instrument and Control Eng s, 34, 248
- yacoob, 1994, Recognizing human facial expression
Cited by
Sorted by citations
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Object oriented architecture for affective multimodal e-learning interfaces
Alepis, E., & Virvou, M. K. (2010) · Intelligent Decision Technologies · doi:10.3233/idt-2010-0078 · Cited by 4
Emotion recognition from facial expression using hybrid-feature extraction
Byun, K.-S., Park, C.-H., & Sim, K.-B. (2004) · Society of Instrument and Control Engineers of Japan · doi:10.11499/sicep.2004.0_101_2 · Cited by 4
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Leung, J. K., Griva, I., & Kennedy, W. G. (2020) · International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management · doi:10.5220/0010056201610168 · Cited by 4
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Wang, Y. (2021) · doi:10.32920/ryerson.14655924 · Cited by 3
Perception of Emotion from Facial Expression and Affective Prosody
Santorelli, N. T. (2022) · Digital Archive @ GSU · doi:10.57709/1061193 · Cited by 4
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Singh, G. (2018) · International Journal of Advanced Research in Computer Science · doi:10.26483/ijarcs.v9i3.6035 · Cited by 3
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Virvou, M. K., Tsihrintzis, G. A., Alepis, E., & Σταθοπούλου, Ι. (2008) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-58603-900-4-250 · Cited by 3
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Discrimination and Implementation of Emotions on Zoomorphic Robot Movements
Matsumaru, T. (2009) · SICE Journal of Control Measurement and System Integration · doi:10.9746/jcmsi.2.365 · Cited by 3
Emotion Based LMS: An Investigation of User Perceptions and Attitudes
Teoh, K.-K., & Lau, S. H. (2015) · International Journal of Learning and Teaching · doi:10.18178/ijlt.1.2.154-160 · Cited by 3
Case Representation of Daily Routine Data Through the Function Behavior Structure (FBS) Framework
Lee, I., Yi, T., Rhim, J., Narangerel, A., Karaji, D. S., & Lee, J. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-58753-0_55 · Cited by 3
Enhancing Emotion Detection Using Metric Learning Approach
Vaish, A., Gupta, S., & Rathee, N. (2018) · Lecture notes in networks and systems · doi:10.1007/978-981-10-8201-6_36 · Cited by 3
A Study of Activity Recognition and Questionable Observer Detection
M., D., & F., A. (2018) · International Journal of Computer Applications · doi:10.5120/ijca2018917855 · Cited by 3
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Nannapaneni, R., & Chatterjee, S. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-33-4893-6_44 · Cited by 3
Emotion recognition and estimation from tracked lip features
Söderström, U., & Li, H. (2004) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 3
Developing emotion recognition for video conference software to support people with autismPreprint
Franzen, M., Gresser, M. S., Müller, T., & Mauser, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.10785 · Cited by 3
Using Technology for Evaluation and Support of Patients’ Emotional States in Healthcare
Wilson, A. S., Prescott, J., & Krasniewicz, J. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-801737-1.00001-9 · Cited by 2
Patrones de reconocimiento de expresiones emocionales usando el Pupilómetro
Cordón, J. R., Navas, J. M. M., Bozal, R. G., & Escandón, C. L. (2015) · Behavior & Law Journal · doi:10.47442/blj.v1.i1.7 · Cited by 2
Emotion aware recommendation engine using ECG signal analysis
Bagavathi, R., Jain, D., Chakraborty, A., & Sharma, A. K. (2021) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt51525.2021.9580129 · Cited by 2
Design of an Emotion Care System for the Elderly Based on Precisely Detecting Emotion States
Dai, W., Chen, H., Zhu, L., Chen, Y., Chen, M., & Zhang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-61546-7_21 · Cited by 2
Abdellaoui, B., Remaida, A., Sabri, Z., Abdellaoui, M., Idrissi, Y. E. B. E., & Moumen, A. (2024) · International Conference on Intelligent Systems and Computer Vision (ISCV) · doi:10.1109/iscv60512.2024.10620124 · Cited by 2
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Jain, I., Jain, M., Sadhwani, P., Gupta, S. C. D., & Sharma, S. (2024) · International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT) · doi:10.1109/ic-sit63503.2024.10862210 · Cited by 2
Facial Emotion Characterization and Detection using Fourier Transform and Machine Learning
Gouru, A., & Suthaharan, S. (2022) · EPiC series in computing · doi:10.29007/7kbw · Cited by 2
Emotional Intelligence in Multimodal Object Oriented User Interfaces
Alepis, E., & Virvou, M. K. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-02937-0_32 · Cited by 2
Towards a non-invasive approach for sensing emotion of patients
Mustafa, V., Tan, B., & Kangsanant, T. (2003) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)33516-4 · Cited by 2
Emotion Recognition Based on Wireless, Physiological and Audiovisual Signals: A Comprehensive Survey
Alabsi, A., Gong, W., & Hawbani, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-9480-6_13 · Cited by 2
Anisuzzaman, D., & Saif, A. F. M. S. (2018) · International Journal of Image Graphics and Signal Processing · doi:10.5815/ijigsp.2018.11.04 · Cited by 2
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Mao, X., & Li, Z. (2012) · InTech eBooks · doi:10.5772/29041 · Cited by 1
Economides, A. A. (2006) · Cited by 1
Iskandarani, M. Z., & Shilbayeh, N. F. (2008) · American Journal of Applied Sciences · doi:10.3844/ajassp.2008.1263.1268 · Cited by 1
Emotional expressiveness in Romanian language
Monica, F. S. (2012) · International Conference on Instrumentation, Measurement, Circuits and Systems · Cited by 1
Structured Connectivity - Face Model for Recognition of the Human Facial Expressions
Dileep, M. R., & Danti, A. (2014) · Cited by 1
Two Level Decision for Recognition of Human Facial Expressions using Neural Network
Prasad, M., Dileep, M. R., & Danti, A. (2016) · International Journal of Data Mining Techniques and Applications · doi:10.20894/ijdmta.102.005.001.006 · Cited by 1
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Desai, S. A., Mistry, P. D., Kahar, J. P., Modak, A. S., & Biwalkar, A. (2021) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces51350.2021.9489060 · Cited by 1
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Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502405 · Cited by 1
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Rezić, A., & Bonetti, A. (2022) · Logopedija · doi:10.31299/log.11.2.3 · Cited by 1
Facial expression recognition using Eigen face approach
Vignesh, M. T., & Umamaheswari, K. M. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns3.5552 · Cited by 1
Fan, R., Liang, M., Yin, M., & Du, J. (2023) · International Conference on Data-driven Optimization of Complex Systems (DOCS) · doi:10.1109/docs60977.2023.10294882 · Cited by 1
Retracted: Facial Expression Analysis: Unveiling the Emotions Through Computer Vision
Uneza, Gupta, D., & Saini, S. (2024) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito61523.2024.10522418 · Cited by 1
RetractedFacial Expressions Based on the Types of Conversation Contents
Minetaki, K., & Ting, I. (2024) · The Review of Socionetwork Strategies · doi:10.1007/s12626-024-00177-z · Cited by 1
Virvou, M. K., Tsihrintzis, G. A., Alepis, E., Σταθοπούλου, Ι., & Kabassi, K. (2015) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-17744-1_2 · Cited by 1
Automatic Classification of Facial Expressions from Video Stream using Decision Tree
Thorat, B., Manza, G., & Yannawar, P. L. (2015) · International Journal of Computer Applications · doi:10.5120/21835-5096 · Cited by 1
A Suggestion to Improve User-Friendliness Based on Monitoring Computer User’s Emotions
Sung, K.-Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58637-3_10 · Cited by 1
Determinant Analysis of Hedging Policy (Study on Non-Financial Company)
Winasseto, M. V., & Utami, W. (2019) · Saudi Journal of Business and Management Studies · doi:10.36348/sjbms.2019.v04i10.004 · Cited by 1
Leung, J. K., Griva, I., & Kennedy, W. G. (2020) · International Journal on Natural Language Computing · doi:10.5121/ijnlc.2020.9402 · Cited by 1
FaceFacts : study of facial features for understanding expression
Choudhury, T. (1999) · Cited by 1
Fusão computacional de observações afetivas.
Cueva, D. R. (2013) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.3.2013.tde-06072014-222834 · Cited by 1
Web-Based Affective Human-Agent Interaction Generation
Mao, X., & Li, Z. (2010) · Studies in computational intelligence · doi:10.1007/978-3-642-13526-2_15 · Cited by 1
Facial shape space using statistical models from surface normals
Ceolin, S. R. (2012) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 1
Toward understanding human expression in human-robot interaction
Miners, W. B. (2006) · UWSpace (University of Waterloo) · Cited by 1
Designing interactive virtual environments with feedback in health applications.
Li, Y. (2018) · doi:10.18297/etd/2983 · Cited by 1
Robustez de las métricas de clasificación de cadencia de tecleo frente a variaciones emocionales
Calot, E. P. (2019) · doi:10.35537/10915/76652 · Cited by 1
Analysis and recording of multimodal data
Van Vulpen, M. (. (2008) · Research Repository (Delft University of Technology)
Dynamic User Representation in Video Phone Applications
Braun, A., & Wichert, R. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-31479-7_30
Emotional cues, emotional signals, and their contrasting effects on listener valence
Christensen, J. (2015) · Audio Mostly Conference (AM) · doi:10.1145/2814895.2814913
Rhetorical video argument analysis
Shahbudin, I., & Jaafar, A. (2013) · International Conference on Information Technology in Asia (CITA) · doi:10.1109/cita.2013.6637578
Recent Developments in Automated Inferencing of Emotional State from Face Images
Σταθοπούλου, Ι., & Tsihrintzis, G. A. (2008) · Communications in computer and information science · doi:10.1007/978-3-540-88655-6_23
Comparing Voice-Only And Video Interviews In A Practical English Classroom At A Korean University
Kobylinski, C. (2014) · Journal of International Education Research (JIER) · doi:10.19030/jier.v10i1.8397
Cordón, J. R., Navas, J. M. M., Bozal, R. G., & Escandón, C. L. (2015)
Facial Expression Recognition System: A Digital Printing Application
Mandal, M., & Banerjee, S. (2014)
Multimodal emotion estimation and emotional synthesize for interaction virtual agent
Yang, M., Tao, J., Li, H., & Mu, K. (2012) · IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) · doi:10.1109/ccis.2012.6664394
Rozpoznawanie stanów emocjonalnych na rysunkach dotykowych twarzy przez kobiety i mężczyzn
Szubielska, M. (2016) · Acta Universitatis Lodziensis Folia Psychologica · doi:10.18778/1427-969x.20.04
Em, A. K., & Dinakaran, K. (2016) · Biomedical Research-tokyo
An Encoder Generative Adversarial Network for Multi-modality Image Recognition
Chen, Y., Yang, C., Zhu, M., & Yang, S. (2018) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2018.8591324
Development of a Korean chatbot system that enables emotional communication with users in real time
Baek, S., & Lee, M. (2021) · Journal of Sensor Science and Technology · doi:10.46670/jsst.2021.30.6.429
Soha (Soft & Hard) Intelligence- A Fulcrum of High Performance of Teachers in HEIs
Parker, S., Zunjur, A., & Ahire, V. S. (2021) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon52575.2021.9686475
Future of Affective Computing and Applied Affective Computing
Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502411 · Cited by 1
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Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502403
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Alepis, E., & Virvou, M. K. (2014) · Intelligent systems reference library · doi:10.1007/978-3-642-53851-3_2
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Sharma, S. (2022) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-4964
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(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets37134
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An Inter-Agreement Study of Automatic Emotion Recognition Techniques from Different Inputs
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Feedback Analyser through Facial Expressions
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Metadata
- Authors
- De Silva, L. C., Miyasato, T., & Nakatsu, R.
- Affiliations
- Advanced Telecommunications Research Institute International (JP)
- ORCID
- Nakatsu, R.
- Venue
- Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications (Cat. No.97TH8237)
- Publisher
- IEEE
- Published
- 2002-11-22
- Volume
- 1
- Pages
- 397-401
- OpenAlex
- W2154739180
- Cited by
- 319
- References
- 9
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 16 · 2023: 16 · 2022: 39 · 2021: 16 · 2020: 18 · 2019: 11 · 2018: 25 · 2017: 11 · 2016: 18 · 2015: 15 · 2014: 8 · 2013: 8 · 2012: 12
- FWCI
- 13.69
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Information, Communications & Signal Processing (ICICS) (publishedVersion)
- National University of Singapore (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- facial emotion recognition, multimodal emotion recognition, vocal emotion expression
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 ›
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