Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space
Abstract
Past research in analysis of human affect has focused on recognition of prototypic expressions of six basic emotions based on posed data acquired in laboratory settings. Recently, there has been a shift toward subtle, continuous, and context-specific interpretations of affective displays recorded in naturalistic and real-world settings, and toward multimodal analysis and recognition of human affect. Converging with this shift, this paper presents, to the best of our knowledge, the first approach in the literature that: 1) fuses facial expression, shoulder gesture, and audio cues for dimensional and continuous prediction of emotions in valence and arousal space, 2) compares the performance of two state-of-the-art machine learning techniques applied to the target problem, the bidirectional Long Short-Term Memory neural networks (BLSTM-NNs), and Support Vector Machines for Regression (SVR), and 3) proposes an output-associative fusion framework that incorporates correlations and covariances between the emotion dimensions. Evaluation of the proposed approach has been done using the spontaneous SAL data from four subjects and subject-dependent leave-one-sequence-out cross validation. The experimental results obtained show that: 1) on average, BLSTM-NNs outperform SVR due to their ability to learn past and future context, 2) the proposed output-associative fusion framework outperforms feature-level and model-level fusion by modeling and learning correlations and patterns between the valence and arousal dimensions, and 3) the proposed system is well able to reproduce the valence and arousal ground truth obtained from human coders.
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LSTM is found to outperform state-of-the-art techniques like SVR.
“Here, LSTMis found to outperform state-of-the-art techniques like SVR (e.g., [150], [151]).”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [150]
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Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334 · see citing works
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Calvo, R. A., & D'Mello, S. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.1 · Cited by 1,893 · cited 1× · see citing works
“For a recent survey of affect detection models, methods, and their applications, reviewed in an interdisciplinary perspective, the reader is referred to [8].”
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Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018 · see citing works
A review of affective computing: From unimodal analysis to multimodal fusion
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644 · see citing works
A multimodal approach to estimating vigilance using EEG and forehead EOG
Zheng, W.-L., & Lu, B.-L. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa5a98 · Cited by 424 · see citing works
Gunes, H., & Schuller, B. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.06.016 · Cited by 360 · see citing works
Self-citationStatic and dynamic 3D facial expression recognition: A comprehensive survey
Sandbach, G., Zafeiriou, S., Pantic, M., & Yin, L. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.06.005 · Cited by 357 · see citing works
Self-citationVerma, G. K., & Tiwary, U. S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.007 · Cited by 354 · see citing works
LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework
Wöllmer, M., Kaiser, M., Eyben, F., Schuller, B., & Rigoll, G. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.03.001 · Cited by 288 · see citing works
AFEW-VA database for valence and arousal estimation in-the-wild
Kossaifi, J., Tzimiropoulos, G., Todorovic, S., & Pantic, M. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.02.001 · Cited by 236 · see citing works
Self-citationAffectAura: an intelligent system for emotional memory
McDuff, D., Karlson, A., Kapoor, A., Roseway, A., & Czerwinski, M. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208525 · Cited by 242 · see citing works
Deep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243 · see citing works
A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face
Lian, H., Lu, C., Li, S., Zhao, Y., Tang, C., & Zong, Y. (2023) · Entropy · doi:10.3390/e25101440 · Cited by 224 · see citing works
“For continuous emotion prediction tasks, a lower MSE signifies that the model’s predictions align more closely with the true emotion values [119].”
BackgroundMethodInfluentialPrediction of asynchronous dimensional emotion ratings from audiovisual and physiological data
Ringeval, F., Eyben, F., Kroupi, E., Yuce, A., Thiran, J.-P., Ebrahimi, T., Lalanne, D., & Schuller, B. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.11.007 · Cited by 202 · see citing works
Shoumy, N. J., Ang, L.-M., Seng, K. P., Rahaman, D. M. M., & Zia, T. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.102447 · Cited by 171 · see citing works
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Li, W., Dong, X., & Wang, Y. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3064918 · Cited by 43 · see citing works
- Authors
- Nicolaou, M. A., Gunes, H., & Pantic, M.
- Affiliations
- Imperial College London (GB)
- ORCID
- Nicolaou, M. A.
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2011-04
- Volume
- 2
- Issue
- 2
- Pages
- 92-105
- ISSN
- 1949-3045 (ISSN-L)
- OpenAlex
- W2164186291
- Cited by
- 450
- References
- 2
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 18 · 2023: 25 · 2022: 24 · 2021: 21 · 2020: 34 · 2019: 59 · 2018: 46 · 2017: 49 · 2016: 32 · 2015: 31 · 2014: 27 · 2013: 37 · 2012: 25
- FWCI
- 39.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Affective Computing (publishedVersion)
- University of Twente Research Information (submittedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- valence-arousal space, multimodal fusion, facial expressions, audio cues, bidirectional LSTM, support vector regression
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