Multimodal human–computer interaction: A survey
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Multimodal sensor combination improves recognition accuracy and robustness.
“multimodal sensor combination was found to improve recognition accuracy and robustness [10].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [10]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382 · see citing works
A survey of socially interactive robots
Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003) · Robotics and Autonomous Systems · doi:10.1016/s0921-8890(02)00372-x · Cited by 3,162 · see citing works
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 · see citing works
Affective multimodal human-computer interaction
Pantic, M., Sebe, N., Cohn, J. F., & Huang, T. (2005) · Proceedings of the 13th annual ACM international conference on Multimedia · doi:10.1145/1101149.1101299 · Cited by 234 · see citing works
Self-citationDe Silva, L. C., & Ng, P. C. (2002) · Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) · doi:10.1109/afgr.2000.840655 · Cited by 196 · see citing works
Communication via eye blinks and eyebrow raises: video-based human-computer interfaces
Grauman, K., Betke, M., Lombardi, J., Gips, J., & Bradski, G. R. (2003) · Universal Access in the Information Society · doi:10.1007/s10209-003-0062-x · Cited by 185 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Content-based multimedia information retrieval: State of the art and challenges
Lew, M. S., Sebe, N., Djeraba, C., & Jain, R. (2006) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/1126004.1126005 · Cited by 1,528 · see citing works
Self-citationMultimodal fusion for multimedia analysis: a survey
Atrey, P. K., Hossain, M. A., El Saddik, A., & Kankanhalli, M. S. (2010) · Multimedia Systems · doi:10.1007/s00530-010-0182-0 · Cited by 1,279 · see citing works
A survey of video datasets for human action and activity recognition
Chaquet, J. M., Carmona, E. J., & Fernández-Caballero, A. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.01.013 · Cited by 455 · 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
Brain–Computer Interface Technologies in the Coming Decades
Lance, B. J., Kerick, S. E., Ries, A. J., Oie, K. S., & McDowell, K. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2184830 · Cited by 231 · see citing works
A survey of affect recognition methods: audio, visual and spontaneous expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2007) · Proceedings of the 9th international conference on Multimodal interfaces · doi:10.1145/1322192.1322216 · Cited by 144 · see citing works
Automatic facial expression recognition on a single 3D face by exploring shape deformation
Gong, B., Wang, Y., Liu, J., & Tang, X. (2009) · Proceedings of the 17th ACM international conference on Multimedia · doi:10.1145/1631272.1631358 · Cited by 116 · see citing works
Human Emotion Recognition With Relational Region-Level Analysis
Li, W., Dong, X., & Wang, Y. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3064918 · Cited by 43 · see citing works
Chojecki, P., Strazdas, D., Przewozny, D., Gard, N., Runde, D., Hoerner, N., Al-Hamadi, A., Eisert, P., & Bosse, S. (2023) · doi:10.20944/preprints202304.0415.v2 · Cited by 7 · see citing works
- Authors
- Jaimes, A., & Sebe, N.
- Affiliations
- Idiap Research Institute (CH)
- University of Amsterdam (NL)
- Corresponding author
- Sebe, Nicu
- ORCID
- Sebe, Nicu
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2007-10
- Volume
- 108
- Issue
- 1-2
- Pages
- 116-134
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W2041649634
- Cited by
- 965
- References
- 6
- Citations by year
- 2026: 26 · 2025: 32 · 2024: 35 · 2023: 42 · 2022: 53 · 2021: 46 · 2020: 38 · 2019: 63 · 2018: 56 · 2017: 49 · 2016: 56 · 2015: 68 · 2014: 60 · 2013: 69 · 2012: 72
- FWCI
- 60.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Vision and Image Understanding (publishedVersion)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- multimodal human-computer interaction, gesture recognition, gaze tracking, facial expression recognition, body modeling, user modeling, task modeling, multimodal fusion
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
