{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:07:02Z","timestamp":1742911622552,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031442094"},{"type":"electronic","value":"9783031442100"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-44210-0_20","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T08:02:34Z","timestamp":1695283354000},"page":"244-258","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Facial Expression Recognition in\u00a0Unconstrained Real-World Scenarios Leveraging Dempster-Shafer Evidence Theory"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8526-4619","authenticated-orcid":false,"given":"Zhenyu","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2920-6099","authenticated-orcid":false,"given":"Tianyi","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shuwang","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7136-1538","authenticated-orcid":false,"given":"Minglei","family":"Shu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299\u20136308 (2017)","DOI":"10.1109\/CVPR.2017.502"},{"issue":"8","key":"20_CR2","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1109\/TPAMI.2016.2515606","volume":"38","author":"CA Corneanu","year":"2016","unstructured":"Corneanu, C.A., Sim\u00f3n, M.O., Cohn, J.F., Guerrero, S.E.: Survey on rgb, 3d, thermal, and multimodal approaches for facial expression recognition: history, trends, and affect-related applications. IEEE Trans. Pattern Anal. Mach. Intell. 38(8), 1548\u20131568 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR3","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780195112719.001.0001","volume-title":"The expression of the emotions in man and animals","author":"C Darwin","year":"1998","unstructured":"Darwin, C., Prodger, P.: The expression of the emotions in man and animals. Oxford University Press, USA (1998)"},{"issue":"2","key":"20_CR4","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/978-3-540-44792-4_3","volume":"219","author":"AP Dempster","year":"2008","unstructured":"Dempster, A.P., et al.: Upper and lower probabilities induced by a multivalued mapping. Classic works of the Dempster-Shafer theory of belief functions 219(2), 57\u201372 (2008)","journal-title":"Classic works of the Dempster-Shafer theory of belief functions"},{"key":"20_CR5","unstructured":"Dhall, A., Goecke, R., Lucey, S., Gedeon, T.: Acted facial expressions in the wild database. Australian National University, Canberra, Australia, Technical Report TR-CS-11 2, 1 (2011)"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Fan, Y., Lu, X., Li, D., Liu, Y.: Video-based emotion recognition using CNN-RNN and c3d hybrid networks. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction, pp. 445\u2013450 (2016)","DOI":"10.1145\/2993148.2997632"},{"issue":"4","key":"20_CR7","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.3390\/s22041524","volume":"22","author":"H Guerdelli","year":"2022","unstructured":"Guerdelli, H., Ferrari, C., Barhoumi, W., Ghazouani, H., Berretti, S.: Macro-and micro-expressions facial datasets: a survey. Sensors 22(4), 1524 (2022)","journal-title":"Sensors"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Jiang, X., et al.: Dfew: a large-scale database for recognizing dynamic facial expressions in the wild. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2881\u20132889 (2020)","DOI":"10.1145\/3394171.3413620"},{"key":"20_CR10","first-page":"1755","volume":"10","author":"DE King","year":"2009","unstructured":"King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755\u20131758 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, S., Kim, S., Park, J., Sohn, K.: Context-aware emotion recognition networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10143\u201310152 (2019)","DOI":"10.1109\/ICCV.2019.01024"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Li, B., Han, Z., Li, H., Fu, H., Zhang, C.: Trustworthy long-tailed classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6970\u20136979 (2022)","DOI":"10.1109\/CVPR52688.2022.00684"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Liu, M., Shan, S., Wang, R., Chen, X.: Learning expressionlets on spatio-temporal manifold for dynamic facial expression recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1749\u20131756 (2014)","DOI":"10.1109\/CVPR.2014.226"},{"key":"20_CR14","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. IEEE (2010)","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Meng, D., Peng, X., Wang, K., Qiao, Y.: Frame attention networks for facial expression recognition in videos. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 3866\u20133870. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803603"},{"key":"20_CR16","unstructured":"Pantic, M., Valstar, M., Rademaker, R., Maat, L.: Web-based database for facial expression analysis. In: 2005 IEEE International Conference on Multimedia and Expo, pp. 5-pp. IEEE (2005)"},{"key":"20_CR17","doi-asserted-by":"publisher","unstructured":"R\u00f6ssler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Niessner, M.: Faceforensics++: learning to detect manipulated facial images. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1\u201311. IEEE Computer Society, Los Alamitos, November 2019. https:\/\/doi.org\/10.1109\/ICCV.2019.00009","DOI":"10.1109\/ICCV.2019.00009"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"20_CR19","unstructured":"Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. In: Advances in Neural Information Processing Systems 31 (2018)"},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489\u20134497 (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"20_CR21","doi-asserted-by":"publisher","unstructured":"Wang, T., Cheng, H., Chow, K.P., Nie, L.: Deep convolutional pooling transformer for deepfake detection. ACM Trans. Multimed. Comput. Commun. Appl. 19(6) (2023). https:\/\/doi.org\/10.1145\/3588574","DOI":"10.1145\/3588574"},{"key":"20_CR22","doi-asserted-by":"publisher","unstructured":"Wang, T., Chow, K.P.: Noise based deepfake detection via multi-head relative-interaction. Proceedings of the AAAI Conference on Artificial Intelligence 37(12), pp. 14548\u201314556 (2023). https:\/\/doi.org\/10.1609\/aaai.v37i12.26701","DOI":"10.1609\/aaai.v37i12.26701"},{"key":"20_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.fsidi.2022.301395","volume":"42","author":"T Wang","year":"2022","unstructured":"Wang, T., Liu, M., Cao, W., Chow, K.P.: Deepfake noise investigation and detection. Forensic Sci. Int. Digital Investigation 42, 301395 (2022). https:\/\/doi.org\/10.1016\/j.fsidi.2022.301395. proceedings of the Twenty-Second Annual DFRWS USA","journal-title":"Forensic Sci. Int. Digital Investigation"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Xiang, L., Ding, G., Han, J.: Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification. In: Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part V 16. pp. 247\u2013263. Springer (2020)","DOI":"10.1007\/978-3-030-58558-7_15"},{"issue":"7","key":"20_CR25","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu, Y., Si, X., Hu, C., Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31(7), 1235\u20131270 (2019)","journal-title":"Neural Comput."},{"key":"20_CR26","unstructured":"Zagoruyko, S., Komodakis, N.: Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928 (2016)"},{"key":"20_CR27","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Wang, T., Shu, M., Wang, Y.: A robust lightweight deepfake detection network using transformers. In: PRICAI 2022: Trends in Artificial Intelligence: 19th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2022, Shanghai, China, November 10\u201313, 2022, Proceedings, Part I, pp. 275\u2013288. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-20862-1_20","DOI":"10.1007\/978-3-031-20862-1_20"},{"issue":"9","key":"20_CR28","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":"20_CR29","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Liu, Q.: Former-dfer: dynamic facial expression recognition transformer. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 1553\u20131561 (2021)","DOI":"10.1145\/3474085.3475292"},{"key":"20_CR30","unstructured":"Zhong, L., Liu, Q., Yang, P., Liu, B., Huang, J., Metaxas, D.N.: Learning active facial patches for expression analysis. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2562\u20132569. IEEE (2012)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44210-0_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T07:10:20Z","timestamp":1703229020000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44210-0_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442094","9783031442100"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44210-0_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"426","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"type of other papers accepted : 9 Abstract","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}