{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:11:46Z","timestamp":1742958706780,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030317225"},{"type":"electronic","value":"9783030317232"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-31723-2_10","type":"book-chapter","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T00:05:31Z","timestamp":1572480331000},"page":"114-125","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Feature-Preserving Based Face Hallucination: Feature Discrimination Versus Pixels Approximation"],"prefix":"10.1007","author":[{"given":"Xiaoyu","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Heng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jungong","family":"Han","sequence":"additional","affiliation":[]},{"given":"Shudong","family":"Hou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,31]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Baker, S., Kanade, T.: Hallucinating faces. In: FG 2000, pp. 83\u201388 (2000)","DOI":"10.1109\/AFGR.2000.840616"},{"key":"10_CR2","unstructured":"Berthelot, D., Schumm, T., Metz, L.: Began: boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703.10717 (2017)"},{"issue":"01","key":"10_CR3","doi-asserted-by":"publisher","first-page":"1750005","DOI":"10.1142\/S021946781750005X","volume":"17","author":"A Bhat","year":"2017","unstructured":"Bhat, A.: Makeup invariant face recognition using features from accelerated segment test and eigen vectors. Int. J. Image Graph. 17(01), 1750005 (2017)","journal-title":"Int. J. Image Graph."},{"key":"10_CR4","unstructured":"Bin, H., Weihai, C., Xingming, W., Chun-Liang, L.: High-quality face image SR using conditional generative adversarial networks. arXiv preprint arXiv:1707.00737 (2017)"},{"key":"10_CR5","unstructured":"Bruna, J., Sprechmann, P., LeCun, Y.: Super-resolution with deep convolutional sufficient statistics. arXiv preprint arXiv:1511.05666 (2015)"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Bulat, A., Tzimiropoulos, G.: Super-FAN: integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 109\u2013117 (2018)","DOI":"10.1109\/CVPR.2018.00019"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Y., Tai, Y., Liu, X., Shen, C., Yang, J.: FSRNet: end-to-end learning face super-resolution with facial priors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2492\u20132501 (2018)","DOI":"10.1109\/CVPR.2018.00264"},{"issue":"2","key":"10_CR8","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR9","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Proceedings conference and Workshop on Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Huang, H., He, R., Sun, Z., Tan, T.: Wavelet-SRNet: a wavelet-based CNN for multi-scale face super resolution. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1689\u20131697 (2017)","DOI":"10.1109\/ICCV.2017.187"},{"key":"10_CR11","unstructured":"Jolicoeur-Martineau, A.: The relativistic discriminator: a key element missing from standard GAN. arXiv preprint arXiv:1807.00734 (2018)"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Kim, J., Kwon Lee, J., Mu Lee, K.: Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1637\u20131645 (2016)","DOI":"10.1109\/CVPR.2016.181"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681\u20134690 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3730\u20133738 (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Sajjadi, M.S., Scholkopf, B., Hirsch, M.: EnhanceNet: single image super-resolution through automated texture synthesis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4491\u20134500 (2017)","DOI":"10.1109\/ICCV.2017.481"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"10_CR18","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"10_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/978-3-030-01240-3_14","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Yu","year":"2018","unstructured":"Yu, X., Fernando, B., Ghanem, B., Porikli, F., Hartley, R.: Face super-resolution guided by facial component heatmaps. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 219\u2013235. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01240-3_14"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Yu, X., Fernando, B., Hartley, R., Porikli, F.: Super-resolving very low-resolution face images with supplementary attributes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 908\u2013917 (2018)","DOI":"10.1109\/CVPR.2018.00101"},{"key":"10_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1007\/978-3-319-46454-1_20","volume-title":"Computer Vision \u2013 ECCV 2016","author":"X Yu","year":"2016","unstructured":"Yu, X., Porikli, F.: Ultra-resolving face images by discriminative generative networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 318\u2013333. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_20"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Yu, X., Porikli, F.: Face hallucination with tiny unaligned images by transformative discriminative neural networks. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.11206"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Yu, X., Porikli, F.: Hallucinating very low-resolution unaligned and noisy face images by transformative discriminative autoencoders. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3760\u20133768 (2017)","DOI":"10.1109\/CVPR.2017.570"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-31723-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:21:24Z","timestamp":1730334084000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-31723-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030317225","9783030317232"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-31723-2_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"31 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.prcv2019.com\/en\/index.html","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":"412","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":"165","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":"40% - 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":"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)"}}]}}