Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order
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?
Researchers report improvements in accuracy by applying intensity normalization (e.g., from 54% to 57% in [94]).
“Researchers report improvements in accuracy by applying intensity normalization (e.g., from 54% to 57% in [94]; see also [98]).”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [94]
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
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 · see citing works
Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark
Dhall, A., Goecke, R., Lucey, S., & Gedeon, T. (2011) · 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) · doi:10.1109/iccvw.2011.6130508 · Cited by 541 · cited 2× · see citing works
“Experiments were performed in the CK+ [4], MMI [54] and SFEW [55] datasets.”
MethodInfluentialMultimodal learning for facial expression recognition
Zhang, W., Zhang, Y., Ma, L., Guan, J., & Gong, S. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.04.012 · Cited by 100 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576 · 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
Deep learning‐based methods for individual recognition in small birds
Ferreira, A. C., Silva, L. R., Renna, F., Brandl, H. B., Renoult, J. P., Farine, D. R., Covas, R., & Doutrelant, C. (2020) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13436 · Cited by 203 · see citing works
Audio-visual emotion fusion (AVEF): A deep efficient weighted approach
Ma, Y., Hao, Y., Chen, M., Chen, J., Lu, P., & Košir, A. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.06.003 · Cited by 155 · see citing works
Pan, B., Hirota, K., Jia, Z., & Dai, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126866 · Cited by 142 · see citing works
Facial Expression Recognition with Identity and Emotion Joint Learning
Li, M., Xu, H., Huang, X., Song, Z., Liu, X., & Li, X. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2880201 · Cited by 128 · see citing works
“Combating deepfakes: multi-LSTM and blockchain as proof of authenticity for digital media,”
Ki Chan, C. C., Kumar, V., Delaney, S., & Gochoo, M. (2020) · 2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) · doi:10.1109/ai4g50087.2020.9311067 · Cited by 48 · see citing works
- Authors
- Lopes, A. T., de Aguiar, E., De Souza, A. F., & Oliveira-Santos, T.
- Affiliations
- Universidade Federal do Espírito Santo (BR)
- Corresponding author
- Oliveira-Santos, Thiago
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2017-01
- Volume
- 61
- Pages
- 610-628
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2506506742
- Cited by
- 799
- References
- 3
- Citations by year
- 2026: 19 · 2025: 35 · 2024: 60 · 2023: 102 · 2022: 101 · 2021: 117 · 2020: 114 · 2019: 115 · 2018: 91 · 2017: 44 · 2013: 1
- FWCI
- 118.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pattern Recognition (publishedVersion)
- Funders
- Universidade Federal do Espírito Santos – UFES (5911/2015)
- Fundação de Amparo Pesquisa do Espírito Santo – FAPES (65883632/14, 53631242/11)
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES (60902841/13, 11012/13-7)
- Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq (552630/2011-0, 312786/2013-1)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial expression recognition, convolutional neural networks, few-shot learning, image preprocessing, real-time recognition, BU-3DFE, JAFFE, CK+
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
