{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T02:17:54Z","timestamp":1771294674232,"version":"3.50.1"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030866075","type":"print"},{"value":"9783030866082","type":"electronic"}],"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\/978-3-030-86608-2_37","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T05:02:56Z","timestamp":1631163776000},"page":"335-345","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Incomplete Texture Repair of Iris Based on Generative Adversarial Networks"],"prefix":"10.1007","author":[{"given":"Yugang","family":"Zeng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huimin","family":"Gan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuang","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"37_CR1","doi-asserted-by":"publisher","first-page":"64517","DOI":"10.1109\/ACCESS.2019.2917153","volume":"7","author":"Y Chen","year":"2019","unstructured":"Chen, Y., et al.: An adaptive CNNs technology for robust iris segmentation. IEEE Access 7, 64517\u201364532 (2019)","journal-title":"IEEE Access"},{"issue":"8","key":"37_CR2","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/83.935036","volume":"10","author":"C Ballester","year":"2001","unstructured":"Ballester, C., et al.: Filling-in by joint interpolation of vector fields and gray levels. IEEE Trans. Image Process. 10(8), 1200\u20131211 (2001)","journal-title":"IEEE Trans. Image Process."},{"key":"37_CR3","doi-asserted-by":"crossref","unstructured":"Bertalmio, M., Bertozzi, A.L., Sapiro, G.: Navier-stokes, fluid dynamics, and image and video inpainting. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001. IEEE, vol. 1, pp. I\u2013I (2001)","DOI":"10.1109\/CVPR.2001.990497"},{"issue":"7","key":"37_CR4","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.1109\/TIP.2009.2019806","volume":"18","author":"RH Chan","year":"2009","unstructured":"Chan, R.H., Wen, Y.W., Yip, A.M.: A fast optimization transfer algorithm for image inpainting in wavelet domains. IEEE Trans. Image Process. 18(7), 1467\u20131476 (2009)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"37_CR5","first-page":"299","volume":"6","author":"Y Zhang","year":"2012","unstructured":"Zhang, Y., et al.: A class of fractional-order variational image inpainting models. Appl. Math. Inf. Sci. 6(2), 299\u2013306 (2012)","journal-title":"Appl. Math. Inf. Sci."},{"key":"37_CR6","doi-asserted-by":"crossref","unstructured":"Pathak, D., et al.: Context encoders: Feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.278"},{"key":"37_CR7","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014)"},{"key":"37_CR8","unstructured":"Zhao, J.B., Mathieu, M., Goroshin, R., et al.: Stacked what where auto-encoders. arXiv:1506.02351 (2016)"},{"key":"37_CR9","doi-asserted-by":"crossref","unstructured":"Yeh, R.A., et al.: Semantic image inpainting with deep generative models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.728"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., Huang, T.S.: Generative image inpainting with contextual attention. arXiv preprint (2018)","DOI":"10.1109\/CVPR.2018.00577"},{"key":"37_CR11","doi-asserted-by":"crossref","unstructured":"Liu, J., Jung, C.: Facial image inpainting using multi-level generative network. In: 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, pp. 1168\u20131173.s (2019)","DOI":"10.1109\/ICME.2019.00204"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Yang, C., et al.: High-resolution image inpainting using multi-scale neural patch synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.434"},{"issue":"10","key":"37_CR13","first-page":"36","volume":"214","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Liu, Z.S.: Research on iris data enhancement method based on GAN. Inf. Commun. 214(10), 36\u201340 (2020)","journal-title":"Inf. Commun."},{"key":"37_CR14","unstructured":"Karras, T., Aila, T., Laine, S., et al.: Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)"},{"issue":"4","key":"37_CR15","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. Trans. Imgage. Proc. 13(4), 600\u2013612 (2004)","journal-title":"Trans. Imgage. Proc."},{"key":"37_CR16","unstructured":"IITD iris database. http:\/\/www.comp.polyu.edu.hk\/~csajaykr\/IITD\/Database_Iris.html"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Hofbauer, H., et al.: A ground truth for iris segmentation. In: 2014 22nd International Conference on Pattern Recognition. IEEE (2014)","DOI":"10.1109\/ICPR.2014.101"},{"key":"37_CR18","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1106\u20131114 (2012)"}],"container-title":["Lecture Notes in Computer Science","Biometric Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86608-2_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T02:27:21Z","timestamp":1725762441000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86608-2_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030866075","9783030866082"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86608-2_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"8 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCBR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Biometric Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"10 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2021","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":"ccbr2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ccbr99.cn\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","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":"53","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":"74% - 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":"2.3","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":"2.1","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":"Full papers are up to 11 pages long.","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)"}}]}}