{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:46:25Z","timestamp":1743036385666,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030697556"},{"type":"electronic","value":"9783030697563"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-69756-3_11","type":"book-chapter","created":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T08:03:17Z","timestamp":1614067397000},"page":"154-167","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Iterative Self-distillation for Precise Facial Landmark Localization"],"prefix":"10.1007","author":[{"given":"Shigenori","family":"Nagae","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yamato","family":"Takeuchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,24]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Koestinger, M., Wohlhart, P., Roth, P.M., Bischof, H.:Annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 2144\u20132151. IEEE (2011)","DOI":"10.1109\/ICCVW.2011.6130513"},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.patcog.2017.04.013","volume":"69","author":"SZ Gilani","year":"2017","unstructured":"Gilani, S.Z., Mian, A., Eastwood, P.: Deep, dense and accurate 3D face correspondence for generating population specific deformable models. Pattern Recogn. 69, 238\u2013250 (2017)","journal-title":"Pattern Recogn."},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Wang, K., Zhao, R., Ji, Q.: A hierarchical generative model for eye image synthesis and eye gaze estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 440\u2013448 (2018)","DOI":"10.1109\/CVPR.2018.00053"},{"key":"11_CR4","doi-asserted-by":"publisher","first-page":"e0197275","DOI":"10.1371\/journal.pone.0197275","volume":"13","author":"Z Wang","year":"2018","unstructured":"Wang, Z., Yang, X., Cheng, K.T.: Accurate face alignment and adaptive patch selection for heart rate estimation from videos under realistic scenarios. PLoS ONE 13, e0197275 (2018)","journal-title":"PLoS ONE"},{"key":"11_CR5","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1109\/34.927467","volume":"23","author":"TF Cootes","year":"2001","unstructured":"Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23, 681\u2013685 (2001)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/s11263-013-0667-3","volume":"107","author":"X Cao","year":"2014","unstructured":"Cao, X., Wei, Y., Wen, F., Sun, J.: Face alignment by explicit shape regression. Int. J. Comput. Vision 107, 177\u2013190 (2014). https:\/\/doi.org\/10.1007\/s11263-013-0667-3","journal-title":"Int. J. Comput. Vision"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Ren, S., Cao, X., Wei, Y., Sun, J.: Face alignment at 3000 FPS via regressing local binary features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1685\u20131692 (2014)","DOI":"10.1109\/CVPR.2014.218"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3476\u20133483 (2013)","DOI":"10.1109\/CVPR.2013.446"},{"key":"11_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1007\/978-3-319-10599-4_7","volume-title":"Computer Vision \u2013 ECCV 2014","author":"Z Zhang","year":"2014","unstructured":"Zhang, Z., Luo, P., Loy, C.C., Tang, X.: Facial landmark detection by deep multi-task learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 94\u2013108. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10599-4_7"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Merget, D., Rock, M., Rigoll, G.: Robust facial landmark detection via a fully-convolutional local-global context network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 781\u2013790 (2018)","DOI":"10.1109\/CVPR.2018.00088"},{"key":"11_CR11","doi-asserted-by":"publisher","first-page":"2546","DOI":"10.1109\/TPAMI.2017.2734779","volume":"40","author":"H Liu","year":"2017","unstructured":"Liu, H., Lu, J., Feng, J., Zhou, J.: Two-stream transformer networks for video-based face alignment. IEEE Trans. Pattern Anal. Mach. Intell. 40, 2546\u20132554 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR12","doi-asserted-by":"publisher","first-page":"2037","DOI":"10.1109\/TPAMI.2017.2745568","volume":"40","author":"E S\u00e1nchez-Lozano","year":"2017","unstructured":"S\u00e1nchez-Lozano, E., Tzimiropoulos, G., Martinez, B., De la Torre, F., Valstar, M.: A functional regression approach to facial landmark tracking. IEEE Trans. Pattern Anal. Mach. Intell. 40, 2037\u20132050 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Belmonte, R., Ihaddadene, N., Tirilly, P., Bilasco, I.M., Djeraba, C.: Video-based face alignment with local motion modeling. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 2106\u20132115. IEEE (2019)","DOI":"10.1109\/WACV.2019.00228"},{"key":"11_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1007\/978-3-030-01249-6_47","volume-title":"Computer Vision \u2013 ECCV 2018","author":"M Guo","year":"2018","unstructured":"Guo, M., Lu, J., Zhou, J.: Dual-agent deep reinforcement learning for deformable face tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 783\u2013799. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_47"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Dong, X., Yu, S.I., Weng, X., Wei, S.E., Yang, Y., Sheikh, Y.: Supervision-by-registration: An unsupervised approach to improve the precision of facial landmark detectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 360\u2013368 (2018)","DOI":"10.1109\/CVPR.2018.00045"},{"key":"11_CR16","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Gao, P., Lu, K., Xue, J.: EfficientFAN: deep knowledge transfer for face alignment. In: Proceedings of the 2020 International Conference on Multimedia Retrieval, pp. 215\u2013223 (2020)","DOI":"10.1145\/3372278.3390692"},{"key":"11_CR18","unstructured":"Furlanello, T., Lipton, Z., Tschannen, M., Itti, L., Anandkumar, A.: Born-again neural networks. In: International Conference on Machine Learning, pp. 1602\u20131611 (2018)"},{"key":"11_CR19","unstructured":"Bagherinezhad, H., Horton, M., Rastegari, M., Farhadi, A.: Label refinery: improving imagenet classification through label progression. arXiv preprint arXiv:1805.02641 (2018)"},{"key":"11_CR20","unstructured":"Kato, N., Li, T., Nishino, K., Uchida, Y.: Improving multi-person pose estimation using label correction. arXiv preprint arXiv:1811.03331 (2018)"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Honari, S., Molchanov, P., Tyree, S., Vincent, P., Pal, C., Kautz, J.: Improving landmark localization with semi-supervised learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1546\u20131555 (2018)","DOI":"10.1109\/CVPR.2018.00167"},{"key":"11_CR22","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.imavis.2016.01.002","volume":"47","author":"C Sagonas","year":"2016","unstructured":"Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: 300 faces in-the-wild challenge: database and results. Image Vis. Comput. 47, 3\u201318 (2016)","journal-title":"Image Vis. Comput."},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: 300 faces in-the-wild challenge: the first facial landmark localization challenge. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 397\u2013403 (2013)","DOI":"10.1109\/ICCVW.2013.59"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: A semi-automatic methodology for facial landmark annotation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 896\u2013903 (2013)","DOI":"10.1109\/CVPRW.2013.132"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Belhumeur, P.N., Jacobs, D.W., Kriegman, D.J., Kumar, N.: Localizing parts of faces using a consensus of exemplars. In: CVPR 2011, pp. 545\u2013552. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995602"},{"key":"11_CR26","unstructured":"Ramanan, D., Zhu, X.:Face detection, pose estimation, and landmark localization in the wild. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2879\u20132886. IEEE (2012)"},{"key":"11_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1007\/978-3-642-33712-3_49","volume-title":"Computer Vision \u2013 ECCV 2012","author":"V Le","year":"2012","unstructured":"Le, V., Brandt, J., Lin, Z., Bourdev, L., Huang, T.S.: Interactive facial feature localization. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 679\u2013692. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33712-3_49"},{"key":"11_CR28","unstructured":"Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: XM2VTSDB: the extended m2vts database. In: Second International Conference on Audio and Video-Based Biometric Person Authentication, vol. 964, pp. 965\u2013966 (1999)"},{"key":"11_CR29","unstructured":"Zhu, S., Li, C., Change Loy, C., Tang, X.: Face alignment by coarse-to-fine shape searching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4998\u20135006 (2015)"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Chrysos, G.G., Antonakos, E., Zafeiriou, S., Snape, P.: Offline deformable face tracking in arbitrary videos. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 1\u20139 (2015)","DOI":"10.1109\/ICCVW.2015.126"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Shen, J., Zafeiriou, S., Chrysos, G.G., Kossaifi, J., Tzimiropoulos, G., Pantic, M.: The first facial landmark tracking in-the-wild challenge: benchmark and results. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 50\u201358 (2015)","DOI":"10.1109\/ICCVW.2015.132"},{"key":"11_CR32","doi-asserted-by":"crossref","unstructured":"Tzimiropoulos, G.: Project-out cascaded regression with an application to face alignment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3659\u20133667 (2015)","DOI":"10.1109\/CVPR.2015.7298989"},{"key":"11_CR33","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":"11_CR34","doi-asserted-by":"crossref","unstructured":"Bulat, A., Tzimiropoulos, G.: How far are we from solving the 2D & 3D face alignment problem? (and a dataset of 230,000 3d facial landmarks). In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1021\u20131030 (2017)","DOI":"10.1109\/ICCV.2017.116"},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Tokui, S., et al.: Chainer: a deep learning framework for accelerating the research cycle. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2002\u20132011. ACM (2019)","DOI":"10.1145\/3292500.3330756"},{"key":"11_CR36","unstructured":"Tokui, S., Oono, K., Hido, S., Clayton, J.: Chainer: a next-generation open source framework for deep learning. In: Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS) (2015)"},{"key":"11_CR37","unstructured":"Akiba, T., Fukuda, K., Suzuki, S.: ChainerMN: scalable distributed deep learning framework. In: Proceedings of Workshop on ML Systems in The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS) (2017)"},{"key":"11_CR38","doi-asserted-by":"crossref","unstructured":"Dong, X., Yan, Y., Ouyang, W., Yang, Y.: Style aggregated network for facial landmark detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 379\u2013388 (2018)","DOI":"10.1109\/CVPR.2018.00047"},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Wang, J., et\u00a0al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2020)","DOI":"10.1109\/TPAMI.2020.2983686"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69756-3_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T08:17:15Z","timestamp":1614068235000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69756-3_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030697556","9783030697563"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69756-3_11","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":"24 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2020.kyoto\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"768","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":"254","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":"0","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":"33% - 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":"3","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":"The conference was held virtually.","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)"}}]}}