{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T22:13:40Z","timestamp":1771539220299,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T00:00:00Z","timestamp":1660089600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T00:00:00Z","timestamp":1660089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s00530-022-00986-8","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T13:03:47Z","timestamp":1660136627000},"page":"139-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Real emotion seeker: recalibrating annotation for facial expression recognition"],"prefix":"10.1007","volume":"29","author":[{"given":"Zehao","family":"Lin","sequence":"first","affiliation":[]},{"given":"Jiahui","family":"She","sequence":"additional","affiliation":[]},{"given":"Qiu","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"key":"986_CR1","first-page":"193","volume":"6","author":"A Mehrabian","year":"2008","unstructured":"Mehrabian, A.: Communication without words. Commun. Theory 6, 193\u2013200 (2008)","journal-title":"Commun. Theory"},{"key":"986_CR2","doi-asserted-by":"crossref","unstructured":"Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-workshops, pp. 94\u2013101 (2010). IEEE","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"986_CR3","unstructured":"Valstar, M., Pantic, M.: Induced disgust, happiness and surprise: an addition to the mmi facial expression database. In: Proceedings of 3rd Internatinal Workshop on EMOTION (satellite of LREC): Corpora for Research on Emotion and Affect, p. 65. Paris, France (2010)"},{"issue":"9","key":"986_CR4","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1016\/j.imavis.2011.07.002","volume":"29","author":"G Zhao","year":"2011","unstructured":"Zhao, G., Huang, X., Taini, M., Li, S.Z., Pietik\u00e4Inen, M.: Facial expression recognition from near-infrared videos. Image Vis. Comput. 29(9), 607\u2013619 (2011)","journal-title":"Image Vis. Comput."},{"key":"986_CR5","doi-asserted-by":"crossref","unstructured":"Li, S., Deng, W., Du, J.: Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2584\u20132593. IEEE (2017)","DOI":"10.1109\/CVPR.2017.277"},{"key":"986_CR6","doi-asserted-by":"crossref","unstructured":"Barsoum, E., Zhang, C., Ferrer, C.C., Zhang, Z.: Training deep networks for facial expression recognition with crowd-sourced label distribution. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction, pp. 279\u2013283 (2016)","DOI":"10.1145\/2993148.2993165"},{"issue":"1","key":"986_CR7","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/TAFFC.2017.2740923","volume":"10","author":"A Mollahosseini","year":"2017","unstructured":"Mollahosseini, A., Hasani, B., Mahoor, M.H.: Affectnet: a database for facial expression, valence, and arousal computing in the wild. IEEE Trans. Affect. Comput. 10(1), 18\u201331 (2017)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"986_CR8","doi-asserted-by":"crossref","unstructured":"Plutchik, R.: A general psychoevolutionary theory of emotion. In: Theories of Emotion, pp. 3\u201333. Elsevier (1980)","DOI":"10.1016\/B978-0-12-558701-3.50007-7"},{"issue":"7","key":"986_CR9","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1109\/TKDE.2016.2545658","volume":"28","author":"X Geng","year":"2016","unstructured":"Geng, X.: Label distribution learning. IEEE Trans. Knowl. Data Eng 28(7), 1734\u20131748 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng"},{"key":"986_CR10","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Xue, H., Geng, X.: Emotion distribution recognition from facial expressions. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 1247\u20131250 (2015)","DOI":"10.1145\/2733373.2806328"},{"key":"986_CR11","doi-asserted-by":"crossref","unstructured":"Chen, S., Wang, J., Chen, Y., Shi, Z., Geng, X., Rui, Y.: Label distribution learning on auxiliary label space graphs for facial expression recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13984\u201313993 (2020)","DOI":"10.1109\/CVPR42600.2020.01400"},{"key":"986_CR12","doi-asserted-by":"crossref","unstructured":"She, J., Hu, Y., Shi, H., Wang, J., Shen, Q., Mei, T.: Dive into ambiguity: Latent distribution mining and pairwise uncertainty estimation for facial expression recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6248\u20136257 (2021)","DOI":"10.1109\/CVPR46437.2021.00618"},{"key":"986_CR13","doi-asserted-by":"publisher","first-page":"131988","DOI":"10.1109\/ACCESS.2020.3010018","volume":"8","author":"T-H Vo","year":"2020","unstructured":"Vo, T.-H., Lee, G.-S., Yang, H.-J., Kim, S.-H.: Pyramid with super resolution for in-the-wild facial expression recognition. IEEE Access 8, 131988\u2013132001 (2020)","journal-title":"IEEE Access"},{"issue":"6","key":"986_CR14","doi-asserted-by":"publisher","first-page":"7714","DOI":"10.3390\/s130607714","volume":"13","author":"D Ghimire","year":"2013","unstructured":"Ghimire, D., Lee, J.: Geometric feature-based facial expression recognition in image sequences using multi-class adaboost and support vector machines. Sensors 13(6), 7714\u20137734 (2013)","journal-title":"Sensors"},{"key":"986_CR15","doi-asserted-by":"crossref","unstructured":"Happy, S., George, A., Routray, A.: A real time facial expression classification system using local binary patterns. In: 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), pp. 1\u20135. IEEE (2012)","DOI":"10.1109\/IHCI.2012.6481802"},{"key":"986_CR16","doi-asserted-by":"crossref","unstructured":"Fabian\u00a0Benitez-Quiroz, C., Srinivasan, R., Martinez, A.M.: Emotionet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5562\u20135570 (2016)","DOI":"10.1109\/CVPR.2016.600"},{"key":"986_CR17","doi-asserted-by":"crossref","unstructured":"Ding, H., Zhou, P., Chellappa, R.: Occlusion-adaptive deep network for robust facial expression recognition. In: 2020 IEEE International Joint Conference on Biometrics (IJCB), pp. 1\u20139. IEEE (2020)","DOI":"10.1109\/IJCB48548.2020.9304923"},{"key":"986_CR18","doi-asserted-by":"publisher","first-page":"4057","DOI":"10.1109\/TIP.2019.2956143","volume":"29","author":"K Wang","year":"2020","unstructured":"Wang, K., Peng, X., Yang, J., Meng, D., Qiao, Y.: Region attention networks for pose and occlusion robust facial expression recognition. IEEE Trans. Image Process. 29, 4057\u20134069 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"986_CR19","doi-asserted-by":"crossref","unstructured":"Song, L., Gong, D., Li, Z., Liu, C., Liu, W.: Occlusion robust face recognition based on mask learning with pairwise differential siamese network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 773\u2013782 (2019)","DOI":"10.1109\/ICCV.2019.00086"},{"key":"986_CR20","doi-asserted-by":"publisher","first-page":"6574","DOI":"10.1109\/TIP.2020.2991549","volume":"29","author":"F Zhang","year":"2020","unstructured":"Zhang, F., Zhang, T., Mao, Q., Xu, C.: A unified deep model for joint facial expression recognition, face synthesis, and face alignment. IEEE Trans. Image Process. 29, 6574\u20136589 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.2991549","journal-title":"IEEE Trans. Image Process."},{"key":"986_CR21","doi-asserted-by":"publisher","first-page":"4445","DOI":"10.1109\/TIP.2020.2972114","volume":"29","author":"F Zhang","year":"2020","unstructured":"Zhang, F., Zhang, T., Mao, Q., Xu, C.: Geometry guided pose-invariant facial expression recognition. IEEE Trans. Image Process. 29, 4445\u20134460 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.2972114","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"986_CR22","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.1109\/TCSVT.2021.3056098","volume":"32","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Zhang, F., Xu, C.: Joint expression synthesis and representation learning for facial expression recognition. IEEE Trans. Circuits Syst. Video Technol. 32(3), 1681\u20131695 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2021.3056098","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"986_CR23","doi-asserted-by":"crossref","unstructured":"Wang, K., Peng, X., Yang, J., Lu, S., Qiao, Y.: Suppressing uncertainties for large-scale facial expression recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6897\u20136906 (2020)","DOI":"10.1109\/CVPR42600.2020.00693"},{"key":"986_CR24","doi-asserted-by":"crossref","unstructured":"Xue, F., Wang, Q., Guo, G.: Transfer: Learning relation-aware facial expression representations with transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3601\u20133610 (2021)","DOI":"10.1109\/ICCV48922.2021.00358"},{"key":"986_CR25","doi-asserted-by":"crossref","unstructured":"Geng, X., Xia, Y.: Head pose estimation based on multivariate label distribution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1837\u20131842 (2014)","DOI":"10.1109\/CVPR.2014.237"},{"key":"986_CR26","doi-asserted-by":"crossref","unstructured":"Su, K., Geng, X.: Soft facial landmark detection by label distribution learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5008\u20135015 (2019)","DOI":"10.1609\/aaai.v33i01.33015008"},{"key":"986_CR27","doi-asserted-by":"publisher","first-page":"2689","DOI":"10.1109\/TPAMI.2020.3038760","volume":"44","author":"K Smith-Miles","year":"2022","unstructured":"Smith-Miles, K., Geng, X.: Revisiting facial age estimation with new insights from instance space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 44, 2689\u20132697 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"issue":"3","key":"986_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11704-020-8272-4","volume":"15","author":"H Zhang","year":"2021","unstructured":"Zhang, H., Zhang, Y., Geng, X.: Practical age estimation using deep label distribution learning. Front. Comput. Sci. 15(3), 1\u20136 (2021)","journal-title":"Front. Comput. Sci."},{"key":"986_CR29","doi-asserted-by":"publisher","first-page":"1632","DOI":"10.1109\/TKDE.2019.2947040","volume":"33","author":"N Xu","year":"2021","unstructured":"Xu, N., Liu, Y.-P., Geng, X.: Label enhancement for label distribution learning. IEEE Trans. Knowl. Data Eng. 33, 1632\u20131643 (2021)","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"986_CR30","unstructured":"Xu, N., Shu, J., Liu, Y., Geng, X.: Variational label enhancement. In: International Conference on Machine Learning, pp. 10597\u201310606. PMLR (2020)"},{"key":"986_CR31","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, M., Geng, X.: Leveraging implicit relative labeling-importance information for effective multi-label learning. In: 2015 IEEE International Conference on Data Mining, pp. 251\u2013260. IEEE (2015)","DOI":"10.1109\/ICDM.2015.41"},{"key":"986_CR32","doi-asserted-by":"crossref","unstructured":"Hou, P., Geng, X., Zhang, M.: Multi-label manifold learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)","DOI":"10.1609\/aaai.v30i1.10258"},{"key":"986_CR33","unstructured":"Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.: Mixmatch: a holistic approach to semi-supervised learning. arXiv preprint arXiv:1905.02249 (2019)"},{"key":"986_CR34","unstructured":"Li, J., Socher, R., Hoi, S.C.H.: Dividemix: Learning with noisy labels as semi-supervised learning. In: International Conference on Learning Representations (2020). https:\/\/openreview.net\/forum?id=HJgExaVtwr"},{"key":"986_CR35","doi-asserted-by":"crossref","unstructured":"Wu, G., Gong, S.: Peer collaborative learning for online knowledge distillation. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i12.17234"},{"key":"986_CR36","volume-title":"Learning in Interactive Environments: Prior Knowledge and New Experience","author":"J Roschelle","year":"1997","unstructured":"Roschelle, J.: Learning in Interactive Environments: Prior Knowledge and New Experience. Princeton, Citeseer (1997)"},{"key":"986_CR37","doi-asserted-by":"crossref","unstructured":"Goodfellow, I.J., Erhan, D., Carrier, P.L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.-H., et al.: Challenges in representation learning: a report on three machine learning contests. In: International Conference on Neural Information Processing, pp. 117\u2013124. Springer (2013)","DOI":"10.1007\/978-3-642-42051-1_16"},{"key":"986_CR38","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)"},{"key":"986_CR39","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res"},{"key":"986_CR40","doi-asserted-by":"crossref","unstructured":"Farzaneh, A.H., Qi, X.: Discriminant distribution-agnostic loss for facial expression recognition in the wild. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 406\u2013407 (2020)","DOI":"10.1109\/CVPRW50498.2020.00211"},{"key":"986_CR41","doi-asserted-by":"crossref","unstructured":"Farzaneh, A.H., Qi, X.: Facial expression recognition in the wild via deep attentive center loss. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2402\u20132411 (2021)","DOI":"10.1109\/WACV48630.2021.00245"},{"key":"986_CR42","doi-asserted-by":"publisher","first-page":"6544","DOI":"10.1109\/TIP.2021.3093397","volume":"30","author":"Z Zhao","year":"2021","unstructured":"Zhao, Z., Liu, Q., Wang, S.: Learning deep global multi-scale and local attention features for facial expression recognition in the wild. IEEE Trans. Image Process. 30, 6544\u20136556 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"986_CR43","doi-asserted-by":"crossref","unstructured":"Ruan, D., Yan, Y., Lai, S., Chai, Z., Shen, C., Wang, H.: Feature decomposition and reconstruction learning for effective facial expression recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7660\u20137669 (2021)","DOI":"10.1109\/CVPR46437.2021.00757"},{"key":"986_CR44","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"3","key":"986_CR45","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-022-00986-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-022-00986-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-022-00986-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T04:37:17Z","timestamp":1673584637000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-022-00986-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,10]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["986"],"URL":"https:\/\/doi.org\/10.1007\/s00530-022-00986-8","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,10]]},"assertion":[{"value":"16 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}