{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T18:25:08Z","timestamp":1776623108399,"version":"3.51.2"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031064265","type":"print"},{"value":"9783031064272","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-06427-2_23","type":"book-chapter","created":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T19:33:35Z","timestamp":1652556815000},"page":"270-283","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Landmark-Guided Conditional GANs for Face Aging"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7113-5066","authenticated-orcid":false,"given":"Xin","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5820-5381","authenticated-orcid":false,"given":"Minglun","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,15]]},"reference":[{"key":"23_CR1","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":"23_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1007\/978-3-319-10599-4_49","volume-title":"Computer Vision \u2013 ECCV 2014","author":"B-C Chen","year":"2014","unstructured":"Chen, B.-C., Chen, C.-S., Hsu, W.H.: Cross-age reference coding for age-invariant face recognition and retrieval. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 768\u2013783. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10599-4_49"},{"issue":"11","key":"23_CR3","doi-asserted-by":"publisher","first-page":"1955","DOI":"10.1109\/TPAMI.2010.36","volume":"32","author":"Y Fu","year":"2010","unstructured":"Fu, Y., Guo, G., Huang, T.S.: Age synthesis and estimation via faces: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 1955\u20131976 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Grother, P.J., Ngan, M.L., Hanaoka, K.K.: Ongoing face recognition vendor test (FRVT) part 2: Identification (2018)","DOI":"10.6028\/NIST.IR.8238"},{"key":"23_CR5","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, pp. 5767\u20135777 (2017)"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Huang, X., Wang, M., Gong, M.: Hierarchically-fused generative adversarial network for text to realistic image synthesis. In: 2019 16th Conference on Computer and Robot Vision (CRV), pp. 73\u201380. IEEE (2019)","DOI":"10.1109\/CRV.2019.00018"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Kemelmacher-Shlizerman, I., Suwajanakorn, S., Seitz, S.M.: Illumination-aware age progression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3334\u20133341 (2014)","DOI":"10.1109\/CVPR.2014.426"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Liu, L., Wang, S., Wan, L., Yu, H.: Multimodal face aging framework via learning disentangled representation. J. Vis. Commun. Image Rep. 83, 103452 (2022)","DOI":"10.1016\/j.jvcir.2022.103452"},{"key":"23_CR10","doi-asserted-by":"publisher","first-page":"2776","DOI":"10.1109\/TIFS.2021.3065499","volume":"16","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Li, Q., Sun, Z., Tan, T.: A 3 GAN: an attribute-aware attentive generative adversarial network for face aging. IEEE Trans. Inf. Forensics Secur. 16, 2776\u20132790 (2021)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"23_CR11","unstructured":"Megvii, I.: Face++ research toolkit (2013)"},{"key":"23_CR12","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)"},{"issue":"4","key":"23_CR13","doi-asserted-by":"publisher","first-page":"603","DOI":"10.3390\/electronics9040603","volume":"9","author":"Q Pham","year":"2020","unstructured":"Pham, Q., Yang, J., Shin, J.: Semi-supervised FaceGAN for face-age progression and regression with synthesized paired images. Electronics 9(4), 603 (2020)","journal-title":"Electronics"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Pumarola, A., Agudo, A., Martinez, A.M., Sanfeliu, A., Moreno-Noguer, F.: GANimation: anatomically-aware facial animation from a single image. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 818\u2013833 (2018)","DOI":"10.1007\/978-3-030-01249-6_50"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Ramanathan, N., Chellappa, R.: Modeling age progression in young faces. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 1, pp. 387\u2013394. IEEE (2006)","DOI":"10.1109\/CVPR.2006.187"},{"issue":"3","key":"23_CR16","first-page":"385","volume":"32","author":"J Suo","year":"2009","unstructured":"Suo, J., Zhu, S.C., Shan, S., Chen, X.: A compositional and dynamic model for face aging. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 385\u2013401 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"23_CR17","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1038\/scientificamerican0280-132","volume":"242","author":"JT Todd","year":"1980","unstructured":"Todd, J.T., Mark, L.S., Shaw, R.E., Pittenger, J.B.: The perception of human growth. Sci. Am. 242(2), 132\u2013145 (1980)","journal-title":"Sci. Am."},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Wang, H., Gong, D., Li, Z., Liu, W.: Decorrelated adversarial learning for age-invariant face recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3527\u20133536 (2019)","DOI":"10.1109\/CVPR.2019.00364"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Recurrent face aging. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2378\u20132386 (2016)","DOI":"10.1109\/CVPR.2016.261"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Wang, Z., Tang, X., Luo, W., Gao, S.: Face aging with identity-preserved conditional generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7939\u20137947 (2018)","DOI":"10.1109\/CVPR.2018.00828"},{"issue":"6","key":"23_CR21","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1109\/TIP.2016.2547587","volume":"25","author":"H Yang","year":"2016","unstructured":"Yang, H., Huang, D., Wang, Y., Wang, H., Tang, Y.: Face aging effect simulation using hidden factor analysis joint sparse representation. IEEE Trans. Image Process. 25(6), 2493\u20132507 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Yao, X., Puy, G., Newson, A., Gousseau, Y., Hellier, P.: High resolution face age editing. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 8624\u20138631. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9412383"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Song, Y., Qi, H.: Age progression\/regression by conditional adversarial autoencoder. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5810\u20135818 (2017)","DOI":"10.1109\/CVPR.2017.463"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06427-2_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T20:34:33Z","timestamp":1727210073000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06427-2_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031064265","9783031064272"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06427-2_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lecce","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2021.org\/","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","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","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":"168","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":"55% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}