{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T20:04:40Z","timestamp":1778011480500,"version":"3.51.4"},"reference-count":168,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T00:00:00Z","timestamp":1584403200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T00:00:00Z","timestamp":1584403200000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s00521-020-04748-3","type":"journal-article","created":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T10:02:34Z","timestamp":1584439354000},"page":"15503-15531","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["A survey on face data augmentation for the training of deep neural networks"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3893-3888","authenticated-orcid":false,"given":"Xiang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiguo","family":"Lian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,3,17]]},"reference":[{"key":"4748_CR1","doi-asserted-by":"crossref","unstructured":"Agianpuye S, Minoi JL (2013) 3d facial expression synthesis: a survey. In: 2013 8th international conference on information technology in Asia (CITA). IEEE, pp 1\u20137","DOI":"10.1109\/CITA.2013.6637552"},{"key":"4748_CR2","doi-asserted-by":"crossref","unstructured":"Alashkar T, Jiang S, Wang S, Fu Y (2017) Examples-rules guided deep neural network for makeup recommendation. In: Thirty-first AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.10626"},{"key":"4748_CR3","unstructured":"Alhaija HA, Mustikovela SK, Mescheder L, Geiger A, Rother C (2017) Augmented reality meets deep learning for car instance segmentation in urban scenes. In: British machine vision conference, vol 1, p 2"},{"key":"4748_CR4","doi-asserted-by":"crossref","unstructured":"Antipov G, Baccouche M, Dugelay JL (2017) Face aging with conditional generative adversarial networks. In: 2017 IEEE international conference on image processing (ICIP). IEEE, pp 2089\u20132093","DOI":"10.1109\/ICIP.2017.8296650"},{"key":"4748_CR5","unstructured":"Antoniou A, Storkey A, Edwards H (2017) Data augmentation generative adversarial networks. ArXiv preprint arXiv:1711.04340"},{"key":"4748_CR6","unstructured":"Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: International conference on machine learning, pp 214\u2013223"},{"key":"4748_CR7","doi-asserted-by":"crossref","unstructured":"Azevedo P, Dos Santos TO, De Aguiar E (2016) An augmented reality virtual glasses try-on system. In: 2016 XVIII symposium on virtual and augmented reality (SVR). IEEE, pp 1\u20139","DOI":"10.1109\/SVR.2016.12"},{"key":"4748_CR8","doi-asserted-by":"crossref","unstructured":"Banerjee S, Bernhard JS, Scheirer WJ, Bowyer KW, Flynn PJ (2017) Srefi: synthesis of realistic example face images. In: 2017 IEEE international joint conference on biometrics (IJCB). IEEE, pp 37\u201345","DOI":"10.1109\/BTAS.2017.8272680"},{"key":"4748_CR9","unstructured":"Banerjee S, Scheirer WJ, Bowyer KW, Flynn, PJ (2018) On hallucinating context and background pixels from a face mask using multi-scale gans. ArXiv preprint arXiv:1811.07104"},{"key":"4748_CR10","doi-asserted-by":"crossref","unstructured":"Bao J, Chen D, Wen F, Li H, Hua G (2017) Cvae-gan: fine-grained image generation through asymmetric training. In: Proceedings of the IEEE international conference on computer vision, pp 2745\u20132754","DOI":"10.1109\/ICCV.2017.299"},{"key":"4748_CR11","doi-asserted-by":"crossref","unstructured":"Bao J, Chen D, Wen F, Li H, Hua G (2018) Towards open-set identity preserving face synthesis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6713\u20136722","DOI":"10.1109\/CVPR.2018.00702"},{"issue":"9","key":"4748_CR12","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1109\/TPAMI.2003.1227983","volume":"25","author":"V Blanz","year":"2003","unstructured":"Blanz V, Vetter T (2003) Face recognition based on fitting a 3d morphable model. IEEE Trans Pattern Anal Mach Intell 25(9):1063\u20131074","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4748_CR13","first-page":"187","volume":"99","author":"V Blanz","year":"1999","unstructured":"Blanz V, Vetter T et al (1999) A morphable model for the synthesis of 3d faces. Siggraph 99:187\u2013194","journal-title":"Siggraph"},{"key":"4748_CR14","unstructured":"Cao J, Hu Y, Yu B, He R, Sun Z (2018) Load balanced gans for multi-view face image synthesis. ArXiv preprint arXiv:1802.07447"},{"key":"4748_CR15","unstructured":"Cao J, Hu Y, Zhang H, He R, Sun Z (2018) Learning a high fidelity pose invariant model for high-resolution face frontalization. In: Advances in neural information processing systems, pp 2872\u20132882"},{"key":"4748_CR16","doi-asserted-by":"crossref","unstructured":"Chang H, Lu J, Yu F, Finkelstein A (2018) Pairedcyclegan: asymmetric style transfer for applying and removing makeup. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 40\u201348","DOI":"10.1109\/CVPR.2018.00012"},{"key":"4748_CR17","unstructured":"Chen W, Xie X, Jia X, Shen L (2018) Texture deformation based generative adversarial networks for face editing. ArXiv preprint arXiv:1812.09832"},{"key":"4748_CR18","unstructured":"Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P (2016) Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In: Advances in neural information processing systems, pp 2172\u20132180"},{"key":"4748_CR19","doi-asserted-by":"crossref","unstructured":"Choi Y, Choi M, Kim M, Ha JW, Kim S, Choo J (2018) Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8789\u20138797","DOI":"10.1109\/CVPR.2018.00916"},{"key":"4748_CR20","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1109\/34.927467","volume":"6","author":"TF Cootes","year":"2001","unstructured":"Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 6:681\u2013685","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4748_CR21","unstructured":"Crispell D, Biris O, Crosswhite N, Byrne J, Mundy JL (2017) Dataset augmentation for pose and lighting invariant face recognition. ArXiv preprint arXiv:1704.04326"},{"key":"4748_CR22","unstructured":"Cubuk E.D, Zoph B, Mane D, Vasudevan V, Le QV (2019) Autoaugment: Learning augmentation strategies from data. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 113\u2013123"},{"key":"4748_CR23","first-page":"342","volume-title":"International conference on artificial neural networks","author":"D Das","year":"2018","unstructured":"Das D, Lee CSG (2018) Graph matching and pseudo-label guided deep unsupervised domain adaptation. In: International conference on artificial neural networks, pp 342\u2013352"},{"key":"4748_CR24","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.engappai.2018.05.001","volume":"73","author":"D Das","year":"2018","unstructured":"Das D, Lee CSG (2018) Sample-to-sample correspondence for unsupervised domain adaptation. Eng Appl Artif Intell 73:80\u201391","journal-title":"Eng Appl Artif Intell"},{"key":"4748_CR25","doi-asserted-by":"crossref","unstructured":"Das D, Lee CSG (2018) Unsupervised domain adaptation using regularized hyper-graph matching. In: Computer vision and pattern recognition","DOI":"10.1109\/ICIP.2018.8451152"},{"key":"4748_CR26","doi-asserted-by":"crossref","unstructured":"Das D, Lee CSG (2019) Zero-shot image recognition using relational matching, adaptation and calibration. In: Computer vision and pattern recognition","DOI":"10.1109\/IJCNN.2019.8852315"},{"key":"4748_CR27","doi-asserted-by":"crossref","unstructured":"Deng J, Cheng S, Xue N, Zhou Y, Zafeiriou S (2018) Uv-gan: Adversarial facial uv map completion for pose-invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7093\u20137102","DOI":"10.1109\/CVPR.2018.00741"},{"key":"4748_CR28","doi-asserted-by":"crossref","unstructured":"Di X, Sindagi VA, Patel VM (2018) Gp-gan: Gender preserving gan for synthesizing faces from landmarks. In: 2018 24th international conference on pattern recognition (ICPR). IEEE, pp 1079\u20131084","DOI":"10.1109\/ICPR.2018.8545081"},{"key":"4748_CR29","doi-asserted-by":"crossref","unstructured":"Ding H, Sricharan K, Chellappa R (2018) Exprgan: facial expression editing with controllable expression intensity. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.12277"},{"key":"4748_CR30","unstructured":"Dinh L, Krueger D, Bengio Y (2014) Nice: non-linear independent components estimation. ArXiv preprint arXiv:1410.8516"},{"key":"4748_CR31","doi-asserted-by":"crossref","unstructured":"Dosovitskiy A, Fischer P, Ilg E, Hausser P, Hazirbas C, Golkov V, Van Der Smagt P, Cremers D, Brox T (2015) Flownet: learning optical flow with convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 2758\u20132766","DOI":"10.1109\/ICCV.2015.316"},{"key":"4748_CR32","unstructured":"Faceapp: Transform your face. https:\/\/www.faceapp.com\/ (2018)"},{"issue":"11","key":"4748_CR33","doi-asserted-by":"publisher","first-page":"3425","DOI":"10.1109\/TIP.2015.2446944","volume":"24","author":"ZH Feng","year":"2015","unstructured":"Feng ZH, Hu G, Kittler J, Christmas W, Wu XJ (2015) Cascaded collaborative regression for robust facial landmark detection trained using a mixture of synthetic and real images with dynamic weighting. IEEE Trans Image Process 24(11):3425\u20133440","journal-title":"IEEE Trans Image Process"},{"key":"4748_CR34","doi-asserted-by":"crossref","unstructured":"Feng ZH, Kittler J, Christmas W, Huber P, Wu XJ (2017) Dynamic attention-controlled cascaded shape regression exploiting training data augmentation and fuzzy-set sample weighting. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2481\u20132490","DOI":"10.1109\/CVPR.2017.392"},{"key":"4748_CR35","unstructured":"Flynn M (2016) Generating faces with deconvolution networks. https:\/\/zo7.github.io\/blog\/2016\/09\/25\/generating-faces.html"},{"key":"4748_CR36","doi-asserted-by":"crossref","unstructured":"Gecer B, Bhattarai B, Kittler J, Kim TK (2018) Semi-supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model. In: Proceedings of the European conference on computer vision (ECCV), pp 217\u2013234","DOI":"10.1007\/978-3-030-01252-6_14"},{"key":"4748_CR37","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672\u20132680"},{"key":"4748_CR38","doi-asserted-by":"crossref","unstructured":"Grover A, Dhar M, Ermon S (2018) Flow-gan: Combining maximum likelihood and adversarial learning in generative models. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11829"},{"key":"4748_CR39","unstructured":"Gu G, Kim ST, Kim K, Baddar WJ, Ro YM (2017) Differential generative adversarial networks: synthesizing non-linear facial variations with limited number of training data. ArXiv preprint arXiv:1711.10267"},{"key":"4748_CR40","unstructured":"Guan S (2018) Tl-gan: transparent latent-space gan. https:\/\/github.com\/SummitKwan\/transparent_latent_gan"},{"key":"4748_CR41","unstructured":"Gulrajani I, Kumar K, Ahmed F, Taiga AA, Visin F, Vazquez D, Courville A (2016) Pixelvae: a latent variable model for natural images. ArXiv preprint arXiv:1611.05013"},{"key":"4748_CR42","unstructured":"Guo D, Sim T (2009) Digital face makeup by example. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 73\u201379"},{"key":"4748_CR43","doi-asserted-by":"crossref","unstructured":"Guo J, Zhu X, Lei Z, Li SZ (2018) Face synthesis for eyeglass-robust face recognition. In: Chinese conference on biometric recognition. Springer, pp 275\u2013284","DOI":"10.1007\/978-3-319-97909-0_30"},{"issue":"6","key":"4748_CR44","doi-asserted-by":"publisher","first-page":"1294","DOI":"10.1109\/TPAMI.2018.2837742","volume":"41","author":"Y Guo","year":"2018","unstructured":"Guo Y, Cai J, Jiang B, Zheng J et al (2018) Cnn-based real-time dense face reconstruction with inverse-rendered photo-realistic face images. IEEE Trans Pattern Anal Mach Intell 41(6):1294\u20131307","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4748_CR45","doi-asserted-by":"crossref","unstructured":"Hassner T, Harel S, Paz E, Enbar R (2015) Effective face frontalization in unconstrained images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4295\u20134304","DOI":"10.1109\/CVPR.2015.7299058"},{"issue":"11","key":"4748_CR46","doi-asserted-by":"publisher","first-page":"5464","DOI":"10.1109\/TIP.2019.2916751","volume":"28","author":"Z He","year":"2019","unstructured":"He Z, Zuo W, Kan M, Shan S, Chen X (2019) Attgan: facial attribute editing by only changing what you want. IEEE Trans Image Process 28(11):5464\u20135478","journal-title":"IEEE Trans Image Process"},{"key":"4748_CR47","unstructured":"Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in neural information processing systems, pp 6626\u20136637"},{"key":"4748_CR48","doi-asserted-by":"crossref","unstructured":"Hong S, Im W, Ryu J, Yang HS (2017) Sspp-dan: Deep domain adaptation network for face recognition with single sample per person. In: 2017 IEEE international conference on image processing (ICIP). IEEE, pp 825\u2013829","DOI":"10.1109\/ICIP.2017.8296396"},{"issue":"1","key":"4748_CR49","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1109\/TIP.2017.2756450","volume":"27","author":"G Hu","year":"2018","unstructured":"Hu G, Peng X, Yang Y, Hospedales TM, Verbeek J (2018) Frankenstein: learning deep face representations using small data. IEEE Trans Image Process 27(1):293\u2013303","journal-title":"IEEE Trans Image Process"},{"key":"4748_CR50","doi-asserted-by":"crossref","unstructured":"Hu G, Yan F, Chan C.H, Deng W, Christmas W, Kittler J, Robertson NM (2016) Face recognition using a unified 3d morphable model. In: European conference on computer vision. Springer, pp 73\u201389","DOI":"10.1007\/978-3-319-46484-8_5"},{"key":"4748_CR51","doi-asserted-by":"crossref","unstructured":"Hu Y, Wu X, Yu B, He R, Sun Z (2018) Pose-guided photorealistic face rotation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8398\u20138406","DOI":"10.1109\/CVPR.2018.00876"},{"key":"4748_CR52","unstructured":"Huang H, He R, Sun Z, Tan T et\u00a0al (2018) Introvae: introspective variational autoencoders for photographic image synthesis. In: Advances in neural information processing systems, pp 52\u201363"},{"key":"4748_CR53","unstructured":"Huang H, Yu PS, Wang C (2018) An introduction to image synthesis with generative adversarial nets. ArXiv preprint arXiv:1803.04469"},{"key":"4748_CR54","doi-asserted-by":"crossref","unstructured":"Huang R, Zhang S, Li T, He R (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","DOI":"10.1109\/ICCV.2017.267"},{"key":"4748_CR55","doi-asserted-by":"crossref","unstructured":"Huber P, Hu G, Tena R, Mortazavian P, Koppen P, Christmas WJ, Ratsch M, Kittler J (2016) A multiresolution 3d morphable face model and fitting framework. In: Proceedings of the 11th international joint conference on computer vision, imaging and computer graphics theory and applications","DOI":"10.5220\/0005669500790086"},{"key":"4748_CR56","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1125\u20131134","DOI":"10.1109\/CVPR.2017.632"},{"key":"4748_CR57","doi-asserted-by":"crossref","unstructured":"Javornik A, Rogers Y, Moutinho AM, Freeman R (2016) Revealing the shopper experience of using a\u201c magic mirror\u201d augmented reality make-up application. In: Conference on designing interactive systems, vol 2016. Association for Computing Machinery (ACM), pp 871\u2013882","DOI":"10.1145\/2901790.2901881"},{"key":"4748_CR58","doi-asserted-by":"crossref","unstructured":"Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European conference on computer vision. Springer, pp 694\u2013711","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"4748_CR59","unstructured":"Juefei-Xu F, Dey R, Bodetti V, Savvides M (2018) Rankgan: a maximum margin ranking gan for generating faces. In: Proceedings of the Asian conference on computer vision (ACCV), vol 4"},{"key":"4748_CR60","unstructured":"Jung A (2017) imgaug. https:\/\/github.com\/aleju\/imgaug"},{"key":"4748_CR61","unstructured":"Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of gans for improved quality, stability, and variation. In: ICLR"},{"key":"4748_CR62","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4401\u20134410","DOI":"10.1109\/CVPR.2019.00453"},{"issue":"4","key":"4748_CR63","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1145\/2897824.2925871","volume":"35","author":"I Kemelmacher-Shlizerman","year":"2016","unstructured":"Kemelmacher-Shlizerman I (2016) Transfiguring portraits. ACM Trans Graph (TOG) 35(4):94","journal-title":"ACM Trans Graph (TOG)"},{"key":"4748_CR64","doi-asserted-by":"crossref","unstructured":"Kemelmacher-Shlizerman I, Suwajanakorn S, Seitz SM (2014) Illumination-aware age progression. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3334\u20133341","DOI":"10.1109\/CVPR.2014.426"},{"key":"4748_CR65","doi-asserted-by":"crossref","unstructured":"Kim D, Hernandez M, Choi J, Medioni G (2017) Deep 3d face identification. In: 2017 IEEE international joint conference on biometrics (IJCB). IEEE, pp 133\u2013142","DOI":"10.1109\/BTAS.2017.8272691"},{"key":"4748_CR66","unstructured":"Kim T, Cha M, Kim H, Lee JK, Kim J (2017) Learning to discover cross-domain relations with generative adversarial networks. In: Proceedings of the 34th international conference on machine learning, volume 70, pp 1857\u20131865. JMLR.org"},{"key":"4748_CR67","unstructured":"Kim T, Kim B, Cha M, Kim J (2017) Unsupervised visual attribute transfer with reconfigurable generative adversarial networks. ArXiv preprint arXiv:1707.09798"},{"key":"4748_CR68","unstructured":"Kingma DP, Dhariwal P (2018) Glow: Generative flow with invertible 1x1 convolutions. In: Advances in neural information processing systems, pp 10236\u201310245"},{"key":"4748_CR69","unstructured":"Kingma DP, Welling M (2014) Auto-encoding variational bayes. In: Proceedings of the 2nd international conference on learning representations (ICLR)"},{"key":"4748_CR70","doi-asserted-by":"crossref","unstructured":"Kitanovski V, Izquierdo E (2011) Augmented reality mirror for virtual facial alterations. In: 2011 18th IEEE international conference on image processing. IEEE, pp 1093\u20131096","DOI":"10.1109\/ICIP.2011.6115616"},{"key":"4748_CR71","unstructured":"Kortylewski A, Schneider A, Gerig T, Egger B, Morel-Forster A, Vetter T (2018) Training deep face recognition systems with synthetic data. ArXiv preprint arXiv:1802.05891"},{"key":"4748_CR72","doi-asserted-by":"crossref","unstructured":"Kossaifi J, Tran L, Panagakis Y, Pantic M (2018) Gagan: geometry-aware generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 878\u2013887","DOI":"10.1109\/CVPR.2018.00098"},{"key":"4748_CR73","unstructured":"Kulkarni TD, Whitney WF, Kohli P, Tenenbaum J (2015) Deep convolutional inverse graphics network. In: Advances in neural information processing systems, pp 2539\u20132547"},{"key":"4748_CR74","unstructured":"Lample G, Zeghidour N, Usunier N, Bordes A, Denoyer L et\u00a0al (2017) Fader networks: manipulating images by sliding attributes. In: Advances in neural information processing systems, pp 5967\u20135976"},{"key":"4748_CR75","unstructured":"Larsen ABL, S\u00f8nderby SK, Larochelle H, Winther O (2015) Autoencoding beyond pixels using a learned similarity metric. In: 33rd International conference on machine learning"},{"key":"4748_CR76","unstructured":"Lee JY, Kang HB (2016) A new digital face makeup method. In: 2016 IEEE international conference on consumer electronics (ICCE). IEEE, pp 129\u2013130"},{"key":"4748_CR77","doi-asserted-by":"publisher","first-page":"5858","DOI":"10.1109\/ACCESS.2017.2696121","volume":"5","author":"J Lemley","year":"2017","unstructured":"Lemley J, Bazrafkan S, Corcoran P (2017) Smart augmentation learning an optimal data augmentation strategy. IEEE Access 5:5858\u20135869","journal-title":"IEEE Access"},{"key":"4748_CR78","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.neucom.2016.12.013","volume":"235","author":"B Leng","year":"2017","unstructured":"Leng B, Yu K, Jingyan Q (2017) Data augmentation for unbalanced face recognition training sets. Neurocomputing 235:10\u201314","journal-title":"Neurocomputing"},{"issue":"1","key":"4748_CR79","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-016-9537-z","volume":"50","author":"L Li","year":"2018","unstructured":"Li L, Peng Y, Qiu G, Sun Z, Liu S (2018) A survey of virtual sample generation technology for face recognition. Artif Intell Rev 50(1):1\u201320","journal-title":"Artif Intell Rev"},{"key":"4748_CR80","unstructured":"Li M, Zuo W, Zhang D (2016) Deep identity-aware transfer of facial attributes. ArXiv preprint arXiv:1610.05586"},{"issue":"11","key":"4748_CR81","doi-asserted-by":"publisher","first-page":"2943","DOI":"10.1109\/TIFS.2019.2907973","volume":"14","author":"P Li","year":"2019","unstructured":"Li P, Hu Y, He R, Sun Z (2019) Global and local consistent wavelet-domain age synthesis. IEEE Trans Inf Forensics Secur 14(11):2943\u20132957","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"4748_CR82","doi-asserted-by":"crossref","unstructured":"Li T, Qian R, Dong C, Liu S, Yan Q, Zhu W, Lin L (2018) Beautygan: instance-level facial makeup transfer with deep generative adversarial network. In: 2018 ACM multimedia conference on multimedia conference. ACM, pp 645\u2013653","DOI":"10.1145\/3240508.3240618"},{"key":"4748_CR83","doi-asserted-by":"crossref","unstructured":"Liu B, Wang X, Dixit M, Kwitt R, Vasconcelos N (2018) Feature space transfer for data augmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9090\u20139098","DOI":"10.1109\/CVPR.2018.00947"},{"key":"4748_CR84","doi-asserted-by":"crossref","unstructured":"Liu F, Zhu R, Zeng D, Zhao Q, Liu X (2018) Disentangling features in 3d face shapes for joint face reconstruction and recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5216\u20135225","DOI":"10.1109\/CVPR.2018.00547"},{"issue":"1s","key":"4748_CR85","first-page":"20","volume":"11","author":"L Liu","year":"2014","unstructured":"Liu L, Xing J, Liu S, Xu H, Zhou X, Yan S (2014) Wow! you are so beautiful today!. ACM Trans Multimed Comput Commun Appl (TOMM) 11(1s):20","journal-title":"ACM Trans Multimed Comput Commun Appl (TOMM)"},{"key":"4748_CR86","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.neucom.2019.03.011","volume":"341","author":"L Liu","year":"2019","unstructured":"Liu L, Zhang H, Ji Y, Wu QJ (2019) Toward ai fashion design: an attribute-gan model for clothing match. Neurocomputing 341:156\u2013167","journal-title":"Neurocomputing"},{"key":"4748_CR87","unstructured":"Liu MY, Breuel T, Kautz J (2017) Unsupervised image-to-image translation networks. In: Advances in neural information processing systems, pp 700\u2013708"},{"key":"4748_CR88","unstructured":"Liu MY, Tuzel O (2016) Coupled generative adversarial networks. In: Advances in neural information processing systems, pp 469\u2013477"},{"key":"4748_CR89","unstructured":"Liu S, Ou X, Qian R, Wang W, Cao X (2016) Makeup like a superstar: deep localized makeup transfer network. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence, IJCAI\u201916. AAAI Press, pp 2568\u20132575"},{"key":"4748_CR90","doi-asserted-by":"crossref","unstructured":"Liu Y, Li Q, Sun Z (2019) Attribute enhanced face aging with wavelet-based generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2019.01215"},{"key":"4748_CR91","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. In: Proceedings of the IEEE international conference on computer vision, pp 3730\u20133738","DOI":"10.1109\/ICCV.2015.425"},{"key":"4748_CR92","doi-asserted-by":"crossref","unstructured":"Lu Y, Tai YW, Tang CK (2018) Attribute-guided face generation using conditional cyclegan. In: Proceedings of the European conference on computer vision (ECCV), pp 282\u2013297","DOI":"10.1007\/978-3-030-01258-8_18"},{"key":"4748_CR93","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.image.2016.03.011","volume":"47","author":"JJ Lv","year":"2016","unstructured":"Lv JJ, Cheng C, Tian GD, Zhou XD, Zhou X (2016) Landmark perturbation-based data augmentation for unconstrained face recognition. Signal Process Image Commun 47:465\u2013475","journal-title":"Signal Process Image Commun"},{"key":"4748_CR94","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.neucom.2016.12.025","volume":"230","author":"JJ Lv","year":"2017","unstructured":"Lv JJ, Shao XH, Huang JS, Zhou XD, Zhou X (2017) Data augmentation for face recognition. Neurocomputing 230:184\u2013196","journal-title":"Neurocomputing"},{"key":"4748_CR95","unstructured":"Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B (2015) Adversarial autoencoders. ArXiv preprint arXiv:1511.05644"},{"key":"4748_CR96","doi-asserted-by":"crossref","unstructured":"Mash R, Borghetti B, Pecarina J (2016) Improved aircraft recognition for aerial refueling through data augmentation in convolutional neural networks. In: International symposium on visual computing. Springer, pp 113\u2013122","DOI":"10.1007\/978-3-319-50835-1_11"},{"key":"4748_CR97","doi-asserted-by":"crossref","unstructured":"Masi I, Rawls S, Medioni G, Natarajan P (2016) Pose-aware face recognition in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4838\u20134846","DOI":"10.1109\/CVPR.2016.523"},{"key":"4748_CR98","unstructured":"Masi I, Tr\u00e3n AT, Hassner T, Leksut JT, Medioni G (2016) Do we really need to collect millions of faces for effective face recognition? In: European conference on computer vision. Springer, pp 579\u2013596"},{"issue":"1","key":"4748_CR99","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s11263-007-0043-2","volume":"75","author":"I Matthews","year":"2007","unstructured":"Matthews I, Xiao J, Baker S (2007) 2d vs. 3d deformable face models: representational power, construction, and real-time fitting. Int J Comput Vision 75(1):93\u2013113","journal-title":"Int J Comput Vision"},{"key":"4748_CR100","doi-asserted-by":"crossref","unstructured":"Menze M, Geiger, A (2015) Object scene flow for autonomous vehicles. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3061\u20133070","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"4748_CR101","unstructured":"Mirza M, Osindero, S (2014) Conditional generative adversarial nets. ArXiv preprint arXiv:1411.1784"},{"key":"4748_CR102","unstructured":"Moniz JRA, Beckham C, Rajotte S, Honari S, Pal C (2018) Unsupervised depth estimation, 3d face rotation and replacement. In: Advances in neural information processing systems, pp 9759\u20139769"},{"key":"4748_CR103","unstructured":"Nguyen TV, Liu L (2017) Smart mirror: Intelligent makeup recommendation and synthesis. In: Proceedings of the 25th ACM international conference on multimedia. ACM, pp 1253\u20131254"},{"key":"4748_CR104","unstructured":"Oo WY (2016) Digital makeup face generation. https:\/\/web.stanford.edu\/class\/ee368\/Project_Autumn_1516\/Reports\/Oo.pdf"},{"key":"4748_CR105","doi-asserted-by":"crossref","unstructured":"Palsson S, Agustsson E, Timofte R, Van\u00a0Gool L (2018) Generative adversarial style transfer networks for face aging. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 2084\u20132092","DOI":"10.1109\/CVPRW.2018.00282"},{"key":"4748_CR106","doi-asserted-by":"crossref","unstructured":"Pandey G, Dukkipati A (2017) Variational methods for conditional multimodal deep learning. In: 2017 international joint conference on neural networks (IJCNN). IEEE, pp 308\u2013315","DOI":"10.1109\/IJCNN.2017.7965870"},{"key":"4748_CR107","doi-asserted-by":"crossref","unstructured":"Parkhi OM, Vedaldi A, Zisserman A et\u00a0al (2015) Deep face recognition. In: The British machine vision conference (BMVC), vol 1, p 6","DOI":"10.5244\/C.29.41"},{"key":"4748_CR108","unstructured":"Perarnau G, Van De\u00a0Weijer J, Raducanu B, \u00c1lvarez JM (2016) Invertible conditional gans for image editing. In: NIPS 2016 workshop on adversarial training"},{"key":"4748_CR109","unstructured":"Pham HX, Wang Y, Pavlovic V (2018) Generative adversarial talking head: Bringing portraits to life with a weakly supervised neural network. ArXiv preprint arXiv:1803.07716"},{"key":"4748_CR110","doi-asserted-by":"crossref","unstructured":"Pumarola A, Agudo A, Martinez AM, Sanfeliu A, Moreno-Noguer F (2018) Ganimation: anatomically-aware facial animation from a single image. In: Proceedings of the European conference on computer vision (ECCV), pp 818\u2013833","DOI":"10.1007\/978-3-030-01249-6_50"},{"key":"4748_CR111","unstructured":"Qiao F, Yao N, Jiao Z, Li Z, Chen H, Wang H (2018) Geometry-contrastive gan for facial expression transfer. ArXiv preprint arXiv:1802.01822"},{"key":"4748_CR112","unstructured":"Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. ArXiv preprint arXiv:1511.06434"},{"key":"4748_CR113","unstructured":"Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training gans. In: Advances in neural information processing systems, pp 2234\u20132242"},{"key":"4748_CR114","unstructured":"Salimans T, Karpathy A, Chen X, Kingma DP (2017) Pixelcnn++: a pixelcnn implementation with discretized logistic mixture likelihood and other modifications. In: ICLR"},{"key":"4748_CR115","unstructured":"Sanchez E, Valstar M (2018) Triple consistency loss for pairing distributions in gan-based face synthesis. ArXiv preprint arXiv:1811.03492"},{"key":"4748_CR116","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815\u2013823","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"4748_CR117","doi-asserted-by":"crossref","unstructured":"Shen W, Liu R (2017) Learning residual images for face attribute manipulation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4030\u20134038","DOI":"10.1109\/CVPR.2017.135"},{"key":"4748_CR118","doi-asserted-by":"crossref","unstructured":"Shen Y, Luo P, Yan J, Wang X, Tang X (2018) Faceid-gan: learning a symmetry three-player gan for identity-preserving face synthesis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 821\u2013830","DOI":"10.1109\/CVPR.2018.00092"},{"key":"4748_CR119","unstructured":"Shen Y, Zhou B, Luo P, Tang X (2018) Facefeat-gan: a two-stage approach for identity-preserving face synthesis. ArXiv preprint arXiv:1812.01288"},{"key":"4748_CR120","doi-asserted-by":"crossref","unstructured":"Shrivastava A, Pfister T, Tuzel O, Susskind J, Wang W, Webb R (2017) Learning from simulated and unsupervised images through adversarial training. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2107\u20132116","DOI":"10.1109\/CVPR.2017.241"},{"key":"4748_CR121","doi-asserted-by":"crossref","unstructured":"Shu X, Tang J, Lai H, Liu L, Yan S (2015) Personalized age progression with aging dictionary. In: Proceedings of the IEEE international conference on computer vision, pp 3970\u20133978","DOI":"10.1109\/ICCV.2015.452"},{"key":"4748_CR122","doi-asserted-by":"crossref","unstructured":"Shu Z, Sahasrabudhe M, Alp\u00a0Guler R, Samaras D, Paragios N, Kokkinos I (2018) Deforming autoencoders: unsupervised disentangling of shape and appearance. In: Proceedings of the European conference on computer vision (ECCV), pp 650\u2013665","DOI":"10.1007\/978-3-030-01249-6_40"},{"key":"4748_CR123","doi-asserted-by":"crossref","unstructured":"Shu Z, Yumer E, Hadap S, Sunkavalli K, Shechtman E, Samaras D (2017) Neural face editing with intrinsic image disentangling. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5541\u20135550","DOI":"10.1109\/CVPR.2017.578"},{"key":"4748_CR124","doi-asserted-by":"publisher","first-page":"66","DOI":"10.3389\/frobt.2018.00066","volume":"5","author":"L Sixt","year":"2018","unstructured":"Sixt L, Wild B, Landgraf T (2018) Rendergan: generating realistic labeled data. Front Robot AI 5:66","journal-title":"Front Robot AI"},{"key":"4748_CR125","unstructured":"Sohn K, Lee H, Yan X (2015) Learning structured output representation using deep conditional generative models. In: Advances in neural information processing systems, pp 3483\u20133491"},{"key":"4748_CR126","doi-asserted-by":"crossref","unstructured":"Song J, Zhang J, Gao L, Liu X, Shen HT (2018) Dual conditional gans for face aging and rejuvenation. In: IJCAI, pp 899\u2013905","DOI":"10.24963\/ijcai.2018\/125"},{"key":"4748_CR127","doi-asserted-by":"crossref","unstructured":"Song L, Lu Z, He R, Sun Z, Tan T (2018) Geometry guided adversarial facial expression synthesis. In: 2018 ACM multimedia conference on multimedia conference. ACM, pp 627\u2013635","DOI":"10.1145\/3240508.3240612"},{"issue":"3","key":"4748_CR128","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1109\/TPAMI.2009.39","volume":"32","author":"J Suo","year":"2010","unstructured":"Suo J, Zhu SC, Shan S, Chen X (2010) A compositional and dynamic model for face aging. IEEE Trans Pattern Anal Mach Intell 32(3):385\u2013401","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4748_CR129","doi-asserted-by":"crossref","unstructured":"Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1701\u20131708","DOI":"10.1109\/CVPR.2014.220"},{"key":"4748_CR130","doi-asserted-by":"crossref","unstructured":"Taylor L, Nitschke G (2018) Improving deep learning using generic data augmentation. In: 2018 IEEE symposium series on computational intelligence (SSCI). IEEE","DOI":"10.1109\/SSCI.2018.8628742"},{"issue":"6","key":"4748_CR131","doi-asserted-by":"publisher","first-page":"183-1","DOI":"10.1145\/2816795.2818056","volume":"34","author":"J Thies","year":"2015","unstructured":"Thies J, Zollh\u00f6fer M, Nie\u00dfner M, Valgaerts L, Stamminger M, Theobalt C (2015) Real-time expression transfer for facial reenactment. ACM Trans Graph 34(6):183-1","journal-title":"ACM Trans Graph"},{"key":"4748_CR132","doi-asserted-by":"crossref","unstructured":"Tian Y, Peng X, Zhao L, Zhang S, Metaxas DN (2018) Cr-gan: learning complete representations for multi-view generation. In: International joint conference on artificial intelligence (IJCAI)","DOI":"10.24963\/ijcai.2018\/131"},{"key":"4748_CR133","doi-asserted-by":"crossref","unstructured":"Tran L, Liu X (2018) Nonlinear 3d face morphable model. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7346\u20137355","DOI":"10.1109\/CVPR.2018.00767"},{"key":"4748_CR134","doi-asserted-by":"crossref","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","DOI":"10.1109\/CVPR.2017.141"},{"issue":"2","key":"4748_CR135","doi-asserted-by":"publisher","first-page":"1","DOI":"10.20870\/IJVR.2010.9.2.2767","volume":"9","author":"DWF van Krevelen","year":"2010","unstructured":"van Krevelen DWF, Poelman R (2010) A survey of augmented reality technologies, applications and limitations. Int J Virtual Real 9(2):1\u201320","journal-title":"Int J Virtual Real"},{"key":"4748_CR136","unstructured":"Van\u00a0den Oord A, Kalchbrenner N, Espeholt L, Vinyals O, Graves A et\u00a0al (2016) Conditional image generation with pixelcnn decoders. In: Advances in neural information processing systems, pp 4790\u20134798"},{"key":"4748_CR137","unstructured":"Van Den\u00a0Oord A, Kalchbrenner N, Kavukcuoglu K (2016) Pixel recurrent neural networks. In: Proceedings of the 33rd international conference on international conference on machine learning-volume 48, ICML\u201916. JMLR.org, pp 1747\u20131756"},{"key":"4748_CR138","unstructured":"Volpi R, Namkoong H, Sener O, Duchi JC, Murino V, Savarese S (2018) Generalizing to unseen domains via adversarial data augmentation. In: Advances in neural information processing systems, pp 5334\u20135344"},{"key":"4748_CR139","unstructured":"Wang J, Perez L (2017) The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Netw Vis Recognit 11"},{"key":"4748_CR140","doi-asserted-by":"crossref","unstructured":"Wang W, Cui Z, Yan Y, Feng J, Yan S, Shu X, Sebe N (2016) Recurrent face aging. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2378\u20132386","DOI":"10.1109\/CVPR.2016.261"},{"key":"4748_CR141","unstructured":"Wang Z, Tang X, Luo W, Gao S (2018) Face aging with identity-preserved conditional generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7939\u20137947"},{"key":"4748_CR142","doi-asserted-by":"crossref","unstructured":"Wiles O, Sophia\u00a0Koepke A, Zisserman A (2018) X2face: A network for controlling face generation using images, audio, and pose codes. In: Proceedings of the European conference on computer vision (ECCV), pp 670\u2013686","DOI":"10.1007\/978-3-030-01261-8_41"},{"key":"4748_CR143","unstructured":"Winston H (2018) Investigating data augmentation strategies for advancing deep learning training. https:\/\/winstonhsu.info\/wp-content\/uploads\/2018\/03\/gtc18-data_aug-180326.pdf"},{"key":"4748_CR144","doi-asserted-by":"crossref","unstructured":"Wu W, Zhang Y, Li C, Qian C, Change\u00a0Loy C (2018) Reenactgan: learning to reenact faces via boundary transfer. In: Proceedings of the European conference on computer vision (ECCV), pp 603\u2013619","DOI":"10.1007\/978-3-030-01246-5_37"},{"issue":"11","key":"4748_CR145","doi-asserted-by":"publisher","first-page":"2884","DOI":"10.1109\/TIFS.2018.2833032","volume":"13","author":"X Wu","year":"2018","unstructured":"Wu X, He R, Sun Z, Tan T (2018) A light cnn for deep face representation with noisy labels. IEEE Trans Inf Forensics Secur 13(11):2884\u20132896","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"4748_CR146","unstructured":"Xiao T, Hong J, Ma J (2018) Dna-gan: learning disentangled representations from multi-attribute images. In: International conference on learning representations workshop 2018"},{"issue":"6","key":"4748_CR147","doi-asserted-by":"publisher","first-page":"7565","DOI":"10.1007\/s11042-017-4661-6","volume":"77","author":"W Xie","year":"2018","unstructured":"Xie W, Shen L, Yang M, Jiang J (2018) Facial expression synthesis with direction field preservation based mesh deformation and lighting fitting based wrinkle mapping. Multimed Tools Appl 77(6):7565\u20137593","journal-title":"Multimed Tools Appl"},{"key":"4748_CR148","doi-asserted-by":"crossref","unstructured":"Yan X, Yang J, Sohn K, Lee H (2016) Attribute2image: conditional image generation from visual attributes. In: European conference on computer vision. Springer, pp 776\u2013791","DOI":"10.1007\/978-3-319-46493-0_47"},{"key":"4748_CR149","doi-asserted-by":"crossref","unstructured":"Yang H, Huang D, Wang Y, Jain AK (2018) Learning face age progression: a pyramid architecture of gans. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 31\u201339","DOI":"10.1109\/CVPR.2018.00011"},{"key":"4748_CR150","unstructured":"Yeh R, Liu Z, Goldman DB, Agarwala A (2016) Semantic facial expression editing using autoencoded flow. ArXiv preprint arXiv:1611.09961"},{"key":"4748_CR151","doi-asserted-by":"crossref","unstructured":"Yin X, Yu X, Sohn K, Liu X, Chandraker M (2017) Towards large-pose face frontalization in the wild. In: Proceedings of the IEEE international conference on computer vision, pp 3990\u20133999","DOI":"10.1109\/ICCV.2017.430"},{"key":"4748_CR152","doi-asserted-by":"crossref","unstructured":"Zhang F, Zhang T, Mao Q, Xu C (2018) Joint pose and expression modeling for facial expression recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3359\u20133368","DOI":"10.1109\/CVPR.2018.00354"},{"key":"4748_CR153","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3691-y","author":"H Zhang","year":"2018","unstructured":"Zhang H, Sun Y, Liu L, Wang X, Li L, Liu W (2018) Clothingout: a category-supervised GAN model for clothing segmentation and retrieval. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-018-3691-y","journal-title":"Neural Comput Appl"},{"key":"4748_CR154","doi-asserted-by":"crossref","unstructured":"Zhang H, Xu T, Li H, Zhang S, Wang X, Huang X, Metaxas DN (2017) Stackgan: text to photo-realistic image synthesis with stacked generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 5907\u20135915","DOI":"10.1109\/ICCV.2017.629"},{"issue":"3","key":"4748_CR155","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1109\/TPAMI.2006.53","volume":"28","author":"L Zhang","year":"2006","unstructured":"Zhang L, Samaras D (2006) Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics. IEEE Trans Pattern Anal Mach Intell 28(3):351\u2013363","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4748_CR156","doi-asserted-by":"crossref","unstructured":"Zhang Z, Song Y, Qi H (2017) Age progression\/regression by conditional adversarial autoencoder. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5810\u20135818","DOI":"10.1109\/CVPR.2017.463"},{"key":"4748_CR157","first-page":"9251","volume":"33","author":"J Zhao","year":"2019","unstructured":"Zhao J, Cheng Y, Cheng Y, Yang Y, Zhao F, Li J, Liu H, Yan S, Feng J (2019) Look across elapse: disentangled representation learning and photorealistic cross-age face synthesis for age-invariant face recognition. Proc AAAI Conf Artif Intell 33:9251\u20139258","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"4748_CR158","doi-asserted-by":"crossref","unstructured":"Zhao J, Cheng Y, Xu Y, Xiong L, Li J, Zhao F, Jayashree K, Pranata S, Shen S, Xing J et\u00a0al (2018) Towards pose invariant face recognition in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2207\u20132216","DOI":"10.1109\/CVPR.2018.00235"},{"key":"4748_CR159","unstructured":"Zhao J, Xiong L, Jayashree PK, Li J, Zhao F, Wang Z, Pranata PS, Shen PS, Yan S, Feng J (2017) Dual-agent gans for photorealistic and identity preserving profile face synthesis. In: Advances in neural information processing systems, pp 66\u201376"},{"issue":"10","key":"4748_CR160","doi-asserted-by":"publisher","first-page":"2380","DOI":"10.1109\/TPAMI.2018.2858819","volume":"41","author":"J Zhao","year":"2018","unstructured":"Zhao J, Xiong L, Li J, Xing J, Yan S, Feng J (2018) 3d-aided dual-agent gans for unconstrained face recognition. IEEE Trans Pattern Anal Mach Intell 41(10):2380\u20132394","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4748_CR161","unstructured":"Zhou S, Xiao T, Yang Y, Feng D, He Q, He W (2017) Genegan: learning object transfiguration and attribute subspace from unpaired data. In: Proceedings of the British machine vision conference 2017"},{"key":"4748_CR162","doi-asserted-by":"crossref","unstructured":"Zhou Y, Shi BE (2017) Photorealistic facial expression synthesis by the conditional difference adversarial autoencoder. In: 2017 seventh international conference on affective computing and intelligent interaction (ACII). IEEE, pp 370\u2013376","DOI":"10.1109\/ACII.2017.8273626"},{"key":"4748_CR163","unstructured":"Zhu H, Zhou Q, Zhang J, Wang JZ (2018) Facial aging and rejuvenation by conditional multi-adversarial autoencoder with ordinal regression. ArXiv preprint arXiv:1804.02740"},{"key":"4748_CR164","unstructured":"Zhu J.Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223\u20132232"},{"key":"4748_CR165","doi-asserted-by":"crossref","unstructured":"Zhu X, Lei Z, Liu X, Shi H, Li SZ (2016) Face alignment across large poses: a 3d solution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 146\u2013155","DOI":"10.1109\/CVPR.2016.23"},{"key":"4748_CR166","unstructured":"Zhu X, Lei Z, Yan J, Yi D, Li SZ (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"},{"key":"4748_CR167","doi-asserted-by":"crossref","unstructured":"Zhu X, Liu Y, Li J, Wan T, Qin Z (2018) Emotion classification with data augmentation using generative adversarial networks. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 349\u2013360","DOI":"10.1007\/978-3-319-93040-4_28"},{"key":"4748_CR168","doi-asserted-by":"crossref","unstructured":"Zhuang L, Yang AY, Zhou Z, Shankar\u00a0Sastry S, Ma Y (2013) Single-sample face recognition with image corruption and misalignment via sparse illumination transfer. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3546\u20133553","DOI":"10.1109\/CVPR.2013.455"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04748-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-020-04748-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04748-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T23:15:14Z","timestamp":1666134914000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-020-04748-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,17]]},"references-count":168,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["4748"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-04748-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,17]]},"assertion":[{"value":"22 August 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliances with ethical standard"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}