{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:28:42Z","timestamp":1740122922584,"version":"3.37.3"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"25","license":[{"start":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T00:00:00Z","timestamp":1680048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T00:00:00Z","timestamp":1680048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Basic Science (Natural Science) Research Projects of Universities in Jiangsu","award":["22KJB520011"],"award-info":[{"award-number":["22KJB520011"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s11042-023-15084-8","type":"journal-article","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T08:02:56Z","timestamp":1680076976000},"page":"39503-39522","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DIQA-FF:dual image quality assessment for face frontalization"],"prefix":"10.1007","volume":"82","author":[{"given":"Xinyi","family":"Duan","sequence":"first","affiliation":[]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3646-8783","authenticated-orcid":false,"given":"Jiuzhen","family":"Liang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"key":"15084_CR1","doi-asserted-by":"publisher","unstructured":"Ajagbe S A, Amuda K A, Oladipupo M A et al (2021) Multi-classification of alzheimer disease on magnetic resonance images (mri) using deep convolutional neural network (dcnn) approaches. In: Association of computer, communication and education for national triumph social and welfare society (ACCENTS), vol 53, p 51. https:\/\/doi.org\/10.19101\/IJACR.2021.1152001","DOI":"10.19101\/IJACR.2021.1152001"},{"key":"15084_CR2","doi-asserted-by":"publisher","unstructured":"Ajagbe S A, Oki O A, Oladipupo M A et al (2022) Investigating the efficiency of deep learning models in bioinspired object detection. In: 2022 international conference on electrical, computer and energy technologies (ICECET). IEEE, pp 1\u20136. https:\/\/doi.org\/10.1109\/ICECET55527.2022.9872568","DOI":"10.1109\/ICECET55527.2022.9872568"},{"key":"15084_CR3","doi-asserted-by":"publisher","unstructured":"Alhlffee MH, Huang YS, Chen YA (2022) 2d facial landmark localization method for multi-view face synthesis image using a two-pathway generative adversarial network approach. Peer J Comput Sci:1\u201328. https:\/\/doi.org\/10.7717\/peerj-cs.897","DOI":"10.7717\/peerj-cs.897"},{"issue":"6","key":"15084_CR4","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/978-1-4899-7488-4_9129","volume":"10","author":"F Alonso-Fernandez","year":"2012","unstructured":"Alonso-Fernandez F, Fierrez J, Ortega-Garcia J (2012) Quality measures in biometric systems. IEEE Secur Privacy 10(6):52\u201362. https:\/\/doi.org\/10.1007\/978-1-4899-7488-4_9129","journal-title":"IEEE Secur Privacy"},{"key":"15084_CR5","doi-asserted-by":"publisher","unstructured":"Boutros F, Fang M, Klemt M et al (2021) CR-FIQA: face image quality assessment by learning sample relative classifiability. pp 1\u201328. arXiv:2112.06592, https:\/\/doi.org\/10.48550\/arXiv.2112.06592","DOI":"10.48550\/arXiv.2112.06592"},{"key":"15084_CR6","doi-asserted-by":"publisher","unstructured":"Cao J, Hu Y, Zhang H et al (2018) Learning a high fidelity pose invariant model for high-resolution face frontalization. Adv Neural Inf Process Syst:31. https:\/\/doi.org\/10.48550\/arXiv.1806.08472","DOI":"10.48550\/arXiv.1806.08472"},{"key":"15084_CR7","doi-asserted-by":"publisher","unstructured":"Deng J, Guo J, Xue N et al (2019) Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4690\u20134699. https:\/\/doi.org\/10.1109\/TPAMI.2021.3087709","DOI":"10.1109\/TPAMI.2021.3087709"},{"key":"15084_CR8","doi-asserted-by":"crossref","unstructured":"Gecer B, Deng J, Zafeiriou S (2021) Ostec: one-shot texture completion. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7628\u20137638","DOI":"10.1109\/CVPR46437.2021.00754"},{"key":"15084_CR9","doi-asserted-by":"crossref","unstructured":"Graedel N, Kasper L, Engel M et al (2021) Feasibility of spiral FMRI based on an LTI gradient model. NeuroImage:118,674\u2013118,674","DOI":"10.1016\/j.neuroimage.2021.118674"},{"issue":"5","key":"15084_CR10","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1016\/j.imavis.2009.08.002","volume":"28","author":"R Gross","year":"2010","unstructured":"Gross R, Matthews I, Cohn J et al (2010) Multi-pie. Image Vis Comput 28(5):807\u2013813. https:\/\/doi.org\/10.1109\/AFGR.2008.4813399","journal-title":"Image Vis Comput"},{"key":"15084_CR11","doi-asserted-by":"publisher","unstructured":"Gu S, Bao J, Chen D et al (2020a) GIQA: generated image quality assessment. In: European conference on computer vision. Springer, pp 369\u2013385. https:\/\/doi.org\/10.48550\/arXiv.2003.08932","DOI":"10.48550\/arXiv.2003.08932"},{"key":"15084_CR12","doi-asserted-by":"publisher","unstructured":"Gu S, Bao J, Chen D et al (2020b) Priorgan: real data prior for generative adversarial nets. pp 1\u201310. arXiv:2006.16990, https:\/\/doi.org\/10.48550\/arXiv.2006.16990","DOI":"10.48550\/arXiv.2006.16990"},{"key":"15084_CR13","doi-asserted-by":"publisher","unstructured":"He H, Liang J, Hou Z et al (2022) Multi-pose face reconstruction and gabor-based dictionary learning for face recognition. Appl Intell:1\u201315. https:\/\/doi.org\/10.1007\/s10489-022-04336-z","DOI":"10.1007\/s10489-022-04336-z"},{"key":"15084_CR14","doi-asserted-by":"crossref","unstructured":"Hernandez-Ortega J, Fierrez J, Serna I et al (2022) FaceQgen: semi-supervised deep learning for face image quality assessment. In: 2021 16th IEEE international conference on automatic face and gesture recognition (FG 2021). IEEE, pp 1\u20138. https:\/\/doi.org\/https:\/\/arxiv.org\/abs\/2201.00770","DOI":"10.1109\/FG52635.2021.9667060"},{"key":"15084_CR15","doi-asserted-by":"publisher","unstructured":"Hu Y, Wu X, Yu B, et al (2018) Pose-guided photorealistic face rotation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8398\u20138406. https:\/\/doi.org\/10.1109\/CVPR.2018.00876","DOI":"10.1109\/CVPR.2018.00876"},{"key":"15084_CR16","unstructured":"Huang GB, Mattar M, Berg T et al (2008) Labeled faces in the wild: a database forstudying face recognition in unconstrained environments. In: Workshop on faces in\u2019Real-Life\u2019Images: detection, alignment, and recognition"},{"key":"15084_CR17","doi-asserted-by":"publisher","unstructured":"Huang R, Zhang S, Li T et al (2017) Beyond face rotation: global and local perception gan for photorealistic and identity preserving frontal view synthesis. In: Proceedings of the IEEE international conference on computer vision, pp 2439\u20132448. https:\/\/doi.org\/10.1109\/ICCV.2017.267","DOI":"10.1109\/ICCV.2017.267"},{"key":"15084_CR18","doi-asserted-by":"crossref","unstructured":"Ju YJ, Lee GH, Hong JH et al (2022) Complete Face Recovery Gan: unsupervised joint face rotation and de-occlusion from a single-view image. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 3711\u20133721","DOI":"10.1109\/WACV51458.2022.00124"},{"key":"15084_CR19","doi-asserted-by":"publisher","unstructured":"Kang Z, Sadeghi M, Horaud R et al (2022) Expression-preserving face frontalization improves visually assisted speech processing. arXiv:2204.02810, https:\/\/doi.org\/10.48550\/arXiv.2204.02810","DOI":"10.48550\/arXiv.2204.02810"},{"key":"15084_CR20","doi-asserted-by":"publisher","unstructured":"Li P, Wu X, Hu Y et al (2019) M2FPA: a multi-yaw multi-pitch high-quality dataset and benchmark for facial pose analysis. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10,043\u201310,051. https:\/\/doi.org\/10.1109\/ICCV.2019.01014","DOI":"10.1109\/ICCV.2019.01014"},{"key":"15084_CR21","doi-asserted-by":"publisher","unstructured":"Li X, Zhang S, Hu J et al (2021) Image-to-image translation via hierarchical style disentanglement. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8639\u20138648. https:\/\/doi.org\/10.48550\/arXiv.2103.01456","DOI":"10.48550\/arXiv.2103.01456"},{"key":"15084_CR22","doi-asserted-by":"publisher","unstructured":"Liu Z, Luo P, Wang X et al (2015) Deep learning face attributes in the wild. In: Proceedings of the IEEE international conference on computer vision, pp 3730\u20133738. https:\/\/doi.org\/10.1109\/ICCV.2015.425","DOI":"10.1109\/ICCV.2015.425"},{"key":"15084_CR23","doi-asserted-by":"publisher","unstructured":"Mao X, Li Q, Xie H et al (2017) Least squares generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2794\u20132802. https:\/\/doi.org\/10.1109\/ICCV.2017.304","DOI":"10.1109\/ICCV.2017.304"},{"key":"15084_CR24","doi-asserted-by":"crossref","unstructured":"Meng Q, Zhao S, Huang Z et al (2021) Magface: a universal representation for face recognition and quality assessment. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14,225\u201314,234","DOI":"10.1109\/CVPR46437.2021.01400"},{"key":"15084_CR25","doi-asserted-by":"crossref","unstructured":"Mostofa M, Saadabadi M S E, Malakshan S R et al (2022) Pose attention-guided profile-to-frontal face recognition. In: 2022 IEEE international joint conference on biometrics (IJCB). IEEE, pp 1\u201310. https:\/\/doi.org\/\/10.48550\/arXiv.2209.07001","DOI":"10.1109\/IJCB54206.2022.10007935"},{"key":"15084_CR26","doi-asserted-by":"publisher","unstructured":"Ou FZ, Chen X, Zhang R et al (2021) SDD-FIQA: unsupervised face image quality assessment with similarity distribution distance. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7670\u20137679. https:\/\/doi.org\/10.48550\/arXiv.2103.05977","DOI":"10.48550\/arXiv.2103.05977"},{"key":"15084_CR27","doi-asserted-by":"publisher","unstructured":"Qian Y, Deng W, Hu J (2019) Unsupervised face normalization with extreme pose and expression in the wild. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 9843\u20139850. https:\/\/doi.org\/10.1109\/CVPR.2019.01008","DOI":"10.1109\/CVPR.2019.01008"},{"key":"15084_CR28","doi-asserted-by":"publisher","unstructured":"Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. Computer ence:1\u201316. https:\/\/doi.org\/10.48550\/arXiv.1511.06434","DOI":"10.48550\/arXiv.1511.06434"},{"key":"15084_CR29","first-page":"1","volume":"31","author":"E Richardson","year":"2018","unstructured":"Richardson E, Weiss Y (2018) On gans and gmms. Adv Neural Inf Process Syst 31:1\u201320","journal-title":"Adv Neural Inf Process Syst"},{"key":"15084_CR30","doi-asserted-by":"publisher","unstructured":"Sengupta S, Chen JC, Castillo C et al (2016) Frontal to profile face verification in the wild. In: 2016 IEEE winter conference on applications of computer vision (WACV), pp 1\u20139. https:\/\/doi.org\/10.1109\/WACV.2016.7477558","DOI":"10.1109\/WACV.2016.7477558"},{"key":"15084_CR31","doi-asserted-by":"publisher","unstructured":"Tian Y, Peng X, Zhao L et al (2018) CR-GAN: learning complete representations for multi-view generation. pp 1\u20137. arXiv:1806.11191, https:\/\/doi.org\/10.48550\/arXiv.1806.11191","DOI":"10.48550\/arXiv.1806.11191"},{"key":"15084_CR32","doi-asserted-by":"publisher","unstructured":"Tian Y, Ni Z, Chen B et al (2022) Generalized visual quality assessment of GAN-generated face images. pp 1\u201312. arXiv:2201.11975, https:\/\/doi.org\/10.48550\/arXiv.2201.11975","DOI":"10.48550\/arXiv.2201.11975"},{"key":"15084_CR33","doi-asserted-by":"publisher","unstructured":"Tran L, Yin X, Liu X (2017) Disentangled representation learning gan for pose-invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1415\u20131424. https:\/\/doi.org\/10.1109\/CVPR.2017.141","DOI":"10.1109\/CVPR.2017.141"},{"key":"15084_CR34","doi-asserted-by":"publisher","unstructured":"Wang H, Wang Y, Zhou Z et al (2018) Cosface: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5265\u20135274. https:\/\/doi.org\/10.1109\/CVPR.2018.00552","DOI":"10.1109\/CVPR.2018.00552"},{"key":"15084_CR35","doi-asserted-by":"publisher","unstructured":"Wang HP, Yu N, Fritz M (2021a) Hijack-gan: unintended-use of pretrained, black-box gans. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 7872\u20137881. https:\/\/doi.org\/10.48550\/arXiv.2011.14107","DOI":"10.48550\/arXiv.2011.14107"},{"key":"15084_CR36","doi-asserted-by":"publisher","unstructured":"Wang X, Li Y, Zhang H et al (2021b) Towards real-world blind face restoration with generative facial prior. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9168\u20139178. https:\/\/doi.org\/10.48550\/arXiv.2101.04061","DOI":"10.48550\/arXiv.2101.04061"},{"key":"15084_CR37","doi-asserted-by":"publisher","unstructured":"Wei Y, Liu M, Wang H et al (2020) Learning flow-based feature warping for face frontalization with illumination inconsistent supervision. In: European conference on computer vision. Springer, pp 558\u2013574. https:\/\/doi.org\/10.48550\/arXiv.2008.06843","DOI":"10.48550\/arXiv.2008.06843"},{"key":"15084_CR38","doi-asserted-by":"crossref","unstructured":"Yin X, Yu X, Sohn K et al (2017) Towards large-pose face frontalization in the wild. In: Proceedings of the IEEE international conference on computer vision, pp 3990\u20133999. https:\/\/doi.org\/https:\/\/arxiv.org\/abs\/1704.06244","DOI":"10.1109\/ICCV.2017.430"},{"key":"15084_CR39","doi-asserted-by":"publisher","unstructured":"Yin Y, Jiang S, Robinson J P et al (2020) Dual-attention GAN for large-pose face frontalization. In: 2020 15th IEEE international conference on automatic face and gesture recognition (FG), vol 2020, pp 249\u2013256. https:\/\/doi.org\/10.48550\/arXiv.2002.07227","DOI":"10.48550\/arXiv.2002.07227"},{"key":"15084_CR40","doi-asserted-by":"crossref","unstructured":"Zeng X, Wu Z, Peng X et al (2022) Joint 3D facial shape reconstruction and texture completion from a single image. Comput Vis Med:239\u2013256","DOI":"10.1007\/s41095-021-0238-4"},{"key":"15084_CR41","doi-asserted-by":"publisher","unstructured":"Zhang Z, Chen X, Wang B et al (2018) Face frontalization using an appearance-flow-based convolutional neural network. IEEE Trans Image Process:2187\u20132199. https:\/\/doi.org\/10.1109\/TIP.2018.2883554","DOI":"10.1109\/TIP.2018.2883554"},{"key":"15084_CR42","doi-asserted-by":"publisher","unstructured":"Zhao J, Cheng Y, Xu Y et al (2018) Towards pose invariant face recognition in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2207\u20132216. https:\/\/doi.org\/10.1109\/CVPR.2018.00235","DOI":"10.1109\/CVPR.2018.00235"},{"key":"15084_CR43","doi-asserted-by":"publisher","unstructured":"Zhou H, Liu J, Liu Z et al (2020) Rotate-and-render: unsupervised photorealistic face rotation from single-view images. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5911\u20135920. https:\/\/doi.org\/10.48550\/arXiv.2003.08124","DOI":"10.48550\/arXiv.2003.08124"},{"key":"15084_CR44","doi-asserted-by":"publisher","unstructured":"Zhu JY, Park T, Isola P et al (2017a) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223\u20132232. https:\/\/doi.org\/10.48550\/arXiv.1703.10593","DOI":"10.48550\/arXiv.1703.10593"},{"key":"15084_CR45","doi-asserted-by":"publisher","unstructured":"Zhu X, Lei Z, Yan J et al (2015) High-fidelity pose and expression normalization for face recognition in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 787\u2013796. https:\/\/doi.org\/10.1109\/CVPR.2015.7298679","DOI":"10.1109\/CVPR.2015.7298679"},{"issue":"1","key":"15084_CR46","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1109\/TPAMI.2017.2778152","volume":"41","author":"X Zhu","year":"2017","unstructured":"Zhu X, Liu X, Lei Z et al (2017b) Face alignment in full pose range: a 3d total solution. IEEE Trans Pattern Anal Machine Intell 41(1):78\u201392. https:\/\/doi.org\/10.1109\/TPAMI.2017.2778152","journal-title":"IEEE Trans Pattern Anal Machine Intell"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15084-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15084-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15084-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T10:23:52Z","timestamp":1696933432000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15084-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,29]]},"references-count":46,"journal-issue":{"issue":"25","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["15084"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15084-8","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2023,3,29]]},"assertion":[{"value":"18 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not containany studies with animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Ethics approval"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Competing interests"}}]}}