Correction to: Automated facial expression recognition using exemplar hybrid deep feature generation technique (Soft Computing, (2023), 27, 13, (8721-8737), 10.1007/s00500-023-08230-9)
| dc.contributor.author | Baygin, Mehmet | |
| dc.contributor.author | Tuncer, Ilknur | |
| dc.contributor.author | Dogan, Sengul | |
| dc.contributor.author | Barua, Prabal Datta | |
| dc.contributor.author | Tuncer, Turker | |
| dc.contributor.author | Cheong, Kang Hao | |
| dc.contributor.author | Acharya, U. Rajendra | |
| dc.date.accessioned | 2026-08-12T16:13:36Z | |
| dc.date.issued | 2026 | |
| dc.department | Fırat Üniversitesi | |
| dc.description.abstract | The author found that the below references were not included in the published article. Kanade T, Cohn JF, Tian Y (2000) Comprehensive database for facial expression analysis. In: Proc-4th IEEE int conf autom face gesture recognition, FG 2000, pp 46–53. https://doi.org/10.1109/AFGR.2000.840611 Lucey P, Cohn JF, Kanade T et al (2010) The extended Cohn–Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE comput soc conf comput vis pattern recognit-work CVPRW 2010, pp 94–101. https://doi.org/10.1109/CVPRW.2010.5543262 Pantic M, Valstar M, Rademaker R, Maat L (2005) Web-based database for facial expression analysis. In: IEEE int conf multimed expo, ICME 2005, pp 317–321. https://doi.org/10.1109/ICME.2005.1521424 Li S, Deng W, Du J (2017) Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2852–2861 Yin L, Wei X, Sun Y et al (2006) A 3D facial expression database for facial behavior research. In: FGR 2006 proc 7th int conf autom face gesture recognit 2006, pp 211–216. https://doi.org/10.1109/FGR.2006.6 Goodfellow IJ, Erhan D, Luc Carrier P et al (2015) Challenges in representation learning: a report on three machine learning contests. Neural Netw 64:59–63. https://doi.org/10.1016/j.neunet.2014.09.005 Zhang Z, Luo P, Loy CC, Tang X (2018) From facial expression recognition to interpersonal relation prediction. Int J Comput Vis 126:550–569. https://doi.org/10.1007/s11263-017-1055-1 Chen L-F, Yen Y-S (2007) Taiwanese facial expression image database. Brain Mapp Lab Inst Brain Sci Natl Yang-Ming Univ Taipei, Taiwan Lyons MJ, Kamachi M, Gyoba J (2020) Coding facial expressions with Gabor wavelets (IVC special issue). arXiv:2009.05938 Lyons MJ (2021) “Excavating AI” re-excavated: debunking a fallacious account of the JAFFE Dataset. arXiv:2107.13998 Lundqvist D, Flykt A, Ohman A (1998) The Karolinska directed emotional faces (KDEF). CD ROM from Dep Clin Neurosci Psychol Sect Karolinska Institutet 2–2 Zhao G, Huang X, Taini M et al (2011) Facial expression recognition from near-infrared videos. Image Vis Comput 29:607–619. https://doi.org/10.1016/j.imavis.2011.07.002 Goldberger J, Hinton GE, Roweis S, Salakhutdinov RR (2004) Neighbourhood components analysis. Advances in neural information processing systems, vol 17, pp 513–520 Kononenko I (1994) Estimating attributes: analysis and extensions of RELIEF. In: European conference on machine learning. Springer, pp 171–182 Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238 Liu H, Setiono R (1995) Chi2: feature selection and discretization of numeric attributes. In: Proceedings of 7th IEEE international conference on tools with artificial intelligence. IEEE, pp 388–391 Kanade T, Cohn JF, Tian Y (2000) Comprehensive database for facial expression analysis. In: Proc-4th IEEE int conf autom face gesture recognition, FG 2000, pp 46–53. https://doi.org/10.1109/AFGR.2000.840611 Lucey P, Cohn JF, Kanade T et al (2010) The extended Cohn–Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE comput soc conf comput vis pattern recognit-work CVPRW 2010, pp 94–101. https://doi.org/10.1109/CVPRW.2010.5543262 Pantic M, Valstar M, Rademaker R, Maat L (2005) Web-based database for facial expression analysis. In: IEEE int conf multimed expo, ICME 2005, pp 317–321. https://doi.org/10.1109/ICME.2005.1521424 Li S, Deng W, Du J (2017) Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2852–2861 Yin L, Wei X, Sun Y et al (2006) A 3D facial expression database for facial behavior research. In: FGR 2006 proc 7th int conf autom face gesture recognit 2006, pp 211–216. https://doi.org/10.1109/FGR.2006.6 Goodfellow IJ, Erhan D, Luc Carrier P et al (2015) Challenges in representation learning: a report on three machine learning contests. Neural Netw 64:59–63. https://doi.org/10.1016/j.neunet.2014.09.005 Zhang Z, Luo P, Loy CC, Tang X (2018) From facial expression recognition to interpersonal relation prediction. Int J Comput Vis 126:550–569. https://doi.org/10.1007/s11263-017-1055-1 Chen L-F, Yen Y-S (2007) Taiwanese facial expression image database. Brain Mapp Lab Inst Brain Sci Natl Yang-Ming Univ Taipei, Taiwan Lyons MJ, Kamachi M, Gyoba J (2020) Coding facial expressions with Gabor wavelets (IVC special issue). arXiv:2009.05938 Lyons MJ (2021) “Excavating AI” re-excavated: debunking a fallacious account of the JAFFE Dataset. arXiv:2107.13998 Lundqvist D, Flykt A, Ohman A (1998) The Karolinska directed emotional faces (KDEF). CD ROM from Dep Clin Neurosci Psychol Sect Karolinska Institutet 2–2 Zhao G, Huang X, Taini M et al (2011) Facial expression recognition from near-infrared videos. Image Vis Comput 29:607–619. https://doi.org/10.1016/j.imavis.2011.07.002 Goldberger J, Hinton GE, Roweis S, Salakhutdinov RR (2004) Neighbourhood components analysis. Advances in neural information processing systems, vol 17, pp 513–520 Kononenko I (1994) Estimating attributes: analysis and extensions of RELIEF. In: European conference on machine learning. Springer, pp 171–182 Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238 Liu H, Setiono R (1995) Chi2: feature selection and discretization of numeric attributes. In: Proceedings of 7th IEEE international conference on tools with artificial intelligence. IEEE, pp 388–391 The original article has been corrected. © Springer-Verlag GmbH Germany, part of Springer Nature 2024. | |
| dc.identifier.doi | 10.1007/s00500-024-09647-6 | |
| dc.identifier.endpage | 1454 | |
| dc.identifier.issn | 1432-7643 | |
| dc.identifier.issue | 2 | |
| dc.identifier.scopus | 2-s2.0-85183711022 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.startpage | 1453 | |
| dc.identifier.uri | https://doi.org/10.1007/s00500-024-09647-6 | |
| dc.identifier.uri | https://hdl.handle.net/11508/43131 | |
| dc.identifier.volume | 30 | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | |
| dc.relation.ispartof | Soft Computing | |
| dc.relation.publicationcategory | Diğer | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.snmz | KA_Scopus_20260511 | |
| dc.title | Correction to: Automated facial expression recognition using exemplar hybrid deep feature generation technique (Soft Computing, (2023), 27, 13, (8721-8737), 10.1007/s00500-023-08230-9) | |
| dc.type | Erratum |







