The DOI matches its Crossref record; 266 of the 266 references it cites were found in a registry or an index.
Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations
Open accessAbstract
Emotion recognition is the ability to precisely infer human emotions from numerous sources and modalities using questionnaires, physical signals, and physiological signals. Recently, motion recognition has gained attention because of its diverse application areas, like affective computing, healthcare, human–robot interactions, and market research. This paper provides a comprehensive and systematic review of emotion recognition techniques of the current decade. The paper includes emotion recognition using physical and physiological signals. Physical signals involve speech and facial expression, while physiological signals include electroencephalogram, electrocardiogram, galvanic skin response, and eye tracking. The paper provides an introduction to various emotion models, stimuli used for emotion elicitation, and the background of existing automated emotion recognition systems. This paper covers comprehensive searching and scanning of well-known datasets followed by design criteria for review. After a thorough analysis and discussion, we selected 142 journal articles using PRISMA guidelines. The review provides a detailed analysis of existing studies and available datasets of emotion recognition. Our review analysis also presented potential challenges in the existing literature and directions for future research.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group* (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00135 · Cited by 37,554
An argument for basic emotions
Ekman, P. (1992) · Cognition and Emotion · doi:10.1080/02699939208411068 · Cited by 9,646
Measuring emotion: The self-assessment manikin and the semantic differential
Bradley, M. M., & Lang, P. J. (1994) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/0005-7916(94)90063-9 · Cited by 9,538
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
(2017) · doi:10.1109/cvpr35066.2017 · Cited by 7,141
DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.15 · Cited by 5,093
Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops · doi:10.1109/cvprw.2010.5543262 · Cited by 4,303
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
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 3,111 · cited 3×
“Some well-known techniques used for UQ involve Bayesian inference, variance-based methods, Monte Carlo methods, probabilistic collocation, ensemble modeling, and bootstrapping [194,198].”
BackgroundMethodSelf-citationZheng, W.-L., & Lu, B.-L. (2015) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2015.2431497 · Cited by 2,595
Presentation and validation of the Radboud Faces Database
Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D. H. J., Hawk, S. T., & van Knippenberg, A. (2010) · Cognition and Emotion · doi:10.1080/02699930903485076 · Cited by 2,420
Cited by
The AI-mediated intimacy economy: a paradigm shift in digital interactions
Bozdağ, A. A. (2025) · AI & SOCIETY · doi:10.1007/s00146-024-02132-6 · Cited by 42
Creating safe environments for children: prevention of trauma in the Extended Verse
Jane Patel, N. (2025) · Empathic Computing · doi:10.70401/ec.2025.0003 · Cited by 4
AI-Driven Learning: Current Applications, Challenges, and Future Prospects
Ismail, I. A., Alkhateeb, M. O., & Alkaabi, M. H. (2025) · Advances in Computational Intelligence and Robotics · doi:10.4018/979-8-3693-7873-1.ch001 · Cited by 1
Metadata
- Authors
- Khare, S. K., Blanes-Vidal, V., Nadimi, E. S., & Acharya, U. R.
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2024-02
- Volume
- 102
- Pages
- 102019
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W4386800155
- Cited by
- 469
- References
- 266
- FWCI
- 23.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations. https://app.cochat.ai/papers/10.1016/j.inffus.2023.102019
- The paper
- Khare, S. K., Blanes-Vidal, V., Nadimi, E. S., & Acharya, U. R. (2024). Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations. Information Fusion. https://doi.org/10.1016/j.inffus.2023.102019
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
