CoChat
Sign Up
Cited by 62Emotion and Mood RecognitionFace recognition and analysisFace and Expression Recognition

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Facial decomposition for expression recognition using texture/shape descriptors and SVM classifier

Lekdioui, Khadija, Messoussi, Rochdi, Ruichek, Yassine, Chaabi, Youness, Touahni, Raja·2017·Signal Processing: Image Communication
DOI 10.1016/j.image.2017.08.001
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1016/j.image.2017.08.001

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

  • On the MUG database, several evaluated LBP variants outperformed the best performing state-of-the-art approach presented in [60].

    “reached a score of 100% outperforming the best performing stateof-the-art approach presented in [60] which reached a score of 99.71%.”

    From Local feature extraction based facial emotion recognition: a survey (2020) · cites this paper as [60]

  • On the MUG database, the best performing state-of-the-art approach presented in [60] reached a score of 99.71%.

    “outperforming the best performing stateof-the-art approach presented in [60] which reached a score of 99.71%.”

    From Local feature extraction based facial emotion recognition: a survey (2020) · cites this paper as [60]

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 32

  1. Gunawan, 2015, Face expression detection on kinect using active appearance model and fuzzy logic, Procedia Comput. Sci., 59, 268, 10.1016/j.procs.2015.07.558 · DOI
  2. Shbib, 2015, Facial expression analysis using active shape model, Int. J. Signal Process. Image Process. Pattern Recognit, 8, 9
  3. Ekman, 1992, An argument for basic emotions, Cogn. Emotion, 6, 169, 10.1080/02699939208411068 · DOI · Paper page
  4. Eligio, 2012, Emotion understanding and performance during computer-supported collaboration, Comput. Hum. Behav., 28, 2046, 10.1016/j.chb.2012.06.001 · DOI
  5. G. Molinari, C. Bozelle, D. Cereghetti, G. Chanel, M. Bétrancourt, T. Pun, Feedback émotionnel et collaboration médiatisée par ordinateur: Quand la perception des interactions est liée aux traits émotionnels, in: Environnements Informatiques pour l’apprentissage humain, Actes de la conférence EIAH, 2013, pp. 305–326.
  6. K. Lekdioui, R. Messoussi, Y. Chaabi, Etude et modélisation des comportements sociaux d’apprenants à distance, à travers l’analyse des traits du visage, in: 7ème Conférence sur les Environnements Informatiques pour l’Apprentissage Humain, EIAH 2015, 2015, pp. 411–413.
  7. R. Nkambou, V. Heritier, Reconnaissance émotionnelle par l’analyse des expressions faciales dans un tuteur intelligent affectif, in: Technologies de l’Information et de la Connaissance dans l’Enseignement Supérieur et l’Industrie, Université de Technologie de Compiègne, 2004, pp. 149–155.
  8. Samal, 1992, Automatic recognition and analysis of human faces and facial expressions: A survey, Pattern Recognit., 25, 65, 10.1016/0031-3203(92)90007-6 · DOI · Paper page
  9. Ojala, 1996, A comparative study of texture measures with classification based on featured distributions, Pattern Recognit., 29, 51, 10.1016/0031-3203(95)00067-4 · DOI · Paper page
  10. Tan, 2010, Enhanced local texture feature sets for face recognition under difficult lighting conditions, IEEE Trans. Image Process., 19, 1635, 10.1109/TIP.2010.2042645 · DOI
  11. Ahmed, 2014, Person-independent facial expression recognition based on compound local binary pattern (clbp), Int. Arab J. Inf. Technol., 11, 195
  12. Poursaberi, 2012, Gauss–laguerre wavelet textural feature fusion with geometrical information for facial expression identification, EURASIP J. Image Video Process., 2012, 1, 10.1186/1687-5281-2012-17 · DOI
  13. V. Rapp, T. Sénéchal, L. Prevost, K. Bailly, H. Salam, R. Seguier, Combinaison de descripteurs hétérogenes pour la reconnaissance de micro-mouvements faciaux, in: RFIA 2012 (Reconnaissance des Formes et Intelligence Artificielle), 2012, pp. 978-2-9539515-2-3.
  14. Shan, 2009, Facial expression recognition based on local binary patterns: A comprehensive study, Image Vis. Comput., 27, 803, 10.1016/j.imavis.2008.08.005 · DOI · Paper page
  15. Carcagnì, 2015, Facial expression recognition and histograms of oriented gradients: a comprehensive study, SpringerPlus, 4, 1, 10.1186/s40064-015-1427-3 · DOI
  16. P. Liu, S. Han, Z. Meng, Y. Tong, Facial expression recognition via a boosted deep belief network, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1805–1812. · DOI
  17. Liu, 2015, Au-inspired deep networks for facial expression feature learning, Neurocomputing, 159, 126, 10.1016/j.neucom.2015.02.011 · DOI
  18. P. Khorrami, T. Paine, T. Huang, Do deep neural networks learn facial action units when doing expression recognition? in: Proceedings of the IEEE International Conference on Computer Vision Workshops, 2015, pp. 19–27. · DOI · Paper page
  19. B. Liu, M. Wang, H. Foroosh, M. Tappen, M. Pensky, Sparse convolutional neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 806–814.
  20. Wang, 2014, A novel automatic facial expression recognition method based on aam, J. Comput., 9, 608, 10.4304/jcp.9.3.608-617 · DOI
  21. J. Chen, Z. Chen, Z. Chi, H. Fu, Facial expression recognition based on facial components detection and hog features, in: International Workshops on Electrical and Computer Engineering Subfields, 2014, pp. 884–888.
  22. Donia, 2014, Spontaneous facial expression recognition based on histogram of oriented gradients descriptor, Comput. Inf. Sci., 7, 31
  23. Happy, 2015, Automatic facial expression recognition using features of salient facial patches, IEEE Trans. Affect. Comput., 6, 1, 10.1109/TAFFC.2014.2386334 · DOI
  24. Youssif, 2011, Automatic facial expression recognition system based on geometric and appearance features, Comput. Inf. Sci., 4, 115
  25. X. Xiong, F. De la Torre, Supervised descent method and its applications to face alignment, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 532–539. · DOI
  26. Bradski, 2000, The opencv library, Dr. Dobbs J., 25, 120
  27. Wu, 2015, Improved local ternary patterns for automatic target recognition in infrared imagery, Sensors, 15, 6399, 10.3390/s150306399 · DOI
  28. M. Ibrahim, M. Alam Efat, S. Kayesh, S.M. Khaled, M. Shoyaib, M. Abdullah-Al-Wadud, Dynamic local ternary pattern for face recognition and verification, in: Proceedings of the International Conference on Computer Engineering and Applications, Tenerife, Spain, Vol. 1012, 2014.
  29. A. Mignon, Apprentissage de métriques et méthodes à noyaux appliqués à la reconnaissance de personnes dans les images, Ph.D. thesis, université de caen 2012.
  30. Vapnik, 1999, An overview of statistical learning theory, IEEE Trans. Neural Netw., 10, 988, 10.1109/72.788640 · DOI
  31. Hsu, 2002, A comparison of methods for multiclass support vector machines, IEEE Trans. Neural Netw., 13, 415, 10.1109/72.991427 · DOI
  32. Chang, 2011, Libsvm: a library for support vector machines, ACM Trans. Intell. Syst. Technol. (TIST), 2, 27

Cited by

Sorted by citations

Metadata

Authors
Lekdioui, K., Messoussi, R., Ruichek, Y., Chaabi, Y., & Touahni, R.
Affiliations
  • Centre National de la Recherche Scientifique (FR)
  • Université Ibn-Tofail (MA)
Corresponding author
Lekdioui, Khadija
ORCID
Venue
Signal Processing: Image Communication
Publisher
Elsevier BV
Published
2017-10
Volume
58
Pages
300-312
ISSN
0923-5965 (ISSN-L)
DOI
10.1016/j.image.2017.08.001
OpenAlex
W2748099705
Cited by
62
References
4
Citations by year
2026: 2 · 2025: 1 · 2023: 6 · 2022: 14 · 2021: 17 · 2020: 8 · 2019: 9 · 2018: 5
FWCI
7.84
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
Signal Processing Image Communication (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › 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, facial landmarks, texture features, shape features, multi-class support vector machine, basic emotions, CK+ dataset, KDEF dataset

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