{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T06:37:58Z","timestamp":1742971078195,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030863395"},{"type":"electronic","value":"9783030863401"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86340-1_7","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T12:03:14Z","timestamp":1631275394000},"page":"80-91","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Facial Expression Recognition by Expression-Specific Representation Swapping"],"prefix":"10.1007","author":[{"given":"Jie","family":"Lei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeyu","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zunlei","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronghua","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, J., Chen, S., Shi, Z., Cai, J.: Facial motion prior networks for facial expression recognition. In: VCIP (2019)","DOI":"10.1109\/VCIP47243.2019.8965826"},{"key":"7_CR2","unstructured":"Chopra, S., Hadsell, R., Lecun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: CVPR (2005)"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I., Zafeiriou, S.: RetinaFace: single-stage dense face localisation in the wild. In: arXiv preprint arXiv:1905.00641 (2019)","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"7_CR4","unstructured":"Feng, Z., et al.: One-sample guided object representation disassembling. In: NeurIPS (2020)"},{"key":"7_CR5","unstructured":"Kim, Y., Yoo, B., Kwak, Y., Choi, C., Kim, J.: Deep generative-contrastive networks for facial expression recognition. In: CVPR (2017)"},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Kuo, C.M., Lai, S.H., Sarkis, M.: A compact deep learning model for robust facial expression recognition. In: CVPRW (2018)","DOI":"10.1109\/CVPRW.2018.00286"},{"key":"7_CR7","unstructured":"Li, S., Deng, W.: Deep facial expression recognition: a survey. IEEE Trans. Affect. Comput. (99) (2018)"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Li, S., Deng, W., Du, J.P.: Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.277"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Li, Y., Zeng, J., Shan, S., Chen, X.: Patch-gated CNN for occlusion-aware facial expression recognition. In: ICPR (2018)","DOI":"10.1109\/ICPR.2018.8545853"},{"key":"7_CR10","unstructured":"Lin, Z., et al.: SPACE: unsupervised object-oriented scene representation via spatial attention and decomposition. In: ICLR (2020)"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Matthews, I.: The extended Cohn-Kanade Dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: CVPRW (2010)","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"7_CR12","unstructured":"Mollahosseini, A., Hasani, B., Mahoor, M.H.: AffectNet: a database for facial expression, valence, and arousal computing in the wild. In: IEEE Transactions on Affective Computing (2017)"},{"key":"7_CR13","unstructured":"Pantic, M.V.: Induced disgust, happiness and surprise: an addition to the mmi facial expression database. In: Proceedings 3rd Intern. Workshop on EMOTION (satellite of LREC): Corpora for Research on Emotion and Affect (2010)"},{"key":"7_CR14","unstructured":"Sohn, K., Yan, X., Lee, H., Arbor, A.: Learning structured output representation using deep conditional generative models. In: NeurIPS (2015)"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Wang, K., Peng, X., Yang, J., Lu, S., Qiao, Y.: Suppressing uncertainties for large-scale facial expression recognition. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00693"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Yang, H., Ciftci, U., Yin, L.: Facial expression recognition by de-expression residue learning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00231"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Yao, A., Cai, D., Hu, P., Wang, S., Chen, Y.: HoloNet: towards robust emotion recognition in the wild. In: ICMI (2016)","DOI":"10.1145\/2993148.2997639"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Zeng, J., Shan, S., Chen, X.: Facial expression recognition with inconsistently annotated datasets. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01261-8_14"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Zhao, G., Huang, X., Taini, M., Li, S.Z., Pietik\u00e4lnen, M.: Facial expression recognition from near-infrared videos. In: Image and Vision Computing (2011)","DOI":"10.1016\/j.imavis.2011.07.002"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Zhao, X., Liang, X., Liu, L., Li, T., Yan, S.: Peak-piloted deep network for facial expression recognition. In: ECCV (2016)","DOI":"10.1007\/978-3-319-46475-6_27"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86340-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T12:04:39Z","timestamp":1631275479000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86340-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863395","9783030863401"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86340-1_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 September 2021","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":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2021\/","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"496","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":"265","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":"4","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":"53% - 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":"3","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":"2.5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was held online due to the COVID-19 pandemic.","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)"}}]}}