{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T01:40:23Z","timestamp":1761183623647,"version":"build-2065373602"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-025-00518-z","type":"journal-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T08:06:33Z","timestamp":1761120393000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Invface: inversion-based synthetic face recognition"],"prefix":"10.1007","volume":"5","author":[{"given":"Zhifang","family":"Sun","sequence":"first","affiliation":[]},{"given":"Sukumar","family":"Letchmunan","sequence":"additional","affiliation":[]},{"given":"Wulfran Fendzi","family":"Mbasso","sequence":"additional","affiliation":[]},{"given":"K.","family":"Tamilselvan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8580-9655","authenticated-orcid":false,"given":"Zhe","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,22]]},"reference":[{"issue":"10s","key":"518_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3507902","volume":"54","author":"H Du","year":"2022","unstructured":"Du H, Shi H, Zeng D, Zhang X-P, Mei T. The elements of end-to-end deep face recognition: a survey of recent advances. ACM Comput Surv (CSUR). 2022;54(10s):1\u201342.","journal-title":"ACM Comput Surv (CSUR)"},{"key":"518_CR2","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.neucom.2020.10.081","volume":"429","author":"M Wang","year":"2021","unstructured":"Wang M, Deng W. Deep face recognition: a survey. Neurocomputing. 2021;429:215\u201344.","journal-title":"Neurocomputing"},{"key":"518_CR3","doi-asserted-by":"crossref","unstructured":"Boutros F, Damer N, Kirchbuchner F, Kuijper A. Elasticface: elastic margin loss for deep face recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2022;1578\u20131587.","DOI":"10.1109\/CVPRW56347.2022.00164"},{"key":"518_CR4","doi-asserted-by":"crossref","unstructured":"Deng J, Guo J, Xue N, Zafeiriou S. Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2019;4690\u20134699.","DOI":"10.1109\/CVPR.2019.00482"},{"key":"518_CR5","doi-asserted-by":"crossref","unstructured":"Huang Y, Wang Y, Tai Y, Liu X, Shen P, Li S, Li J, Huang F, Curricularface: adaptive curriculum learning loss for deep face recognition, in: proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2020;5901\u20135910.","DOI":"10.1109\/CVPR42600.2020.00594"},{"key":"518_CR6","doi-asserted-by":"crossref","unstructured":"Kim M, Jain AK, Liu X. Adaface: Quality adaptive margin for face recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2022; pp. 18750\u201318759.","DOI":"10.1109\/CVPR52688.2022.01819"},{"key":"518_CR7","doi-asserted-by":"crossref","unstructured":"Wang H, Wang Y, Zhou Z, Ji X, Gong D, Zhou J, Li Z, Liu W. Cosface: Large margin cosine loss for deep face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018; pp. 5265\u20135274.","DOI":"10.1109\/CVPR.2018.00552"},{"key":"518_CR8","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"518_CR9","doi-asserted-by":"crossref","unstructured":"Duta IC, Liu L, Zhu F, Shao L. Improved residual networks for image and video recognition. In: 2020 25th International conference on pattern recognition (ICPR), IEEE, 2021; pp. 9415\u20139422.","DOI":"10.1109\/ICPR48806.2021.9412193"},{"key":"518_CR10","doi-asserted-by":"crossref","unstructured":"Cao Q, Shen L, Xie W, Parkhi OM, Zisserman A. Vggface2: a dataset for recognising faces across pose and age. In: 13th IEEE international conference on automatic face & gesture recognition (fg 2018). IEEE. 2018;2018:67\u201374.","DOI":"10.1109\/FG.2018.00020"},{"key":"518_CR11","doi-asserted-by":"crossref","unstructured":"Bansal A, Nanduri A, Castillo CD, Ranjan R, Chellappa R. Umdfaces: an annotated face dataset for training deep networks. In: IEEE international joint conference on biometrics (IJCB). IEEE. 2017;2017:464\u201373.","DOI":"10.1109\/BTAS.2017.8272731"},{"key":"518_CR12","doi-asserted-by":"crossref","unstructured":"Guo Y, Zhang L, Hu Y, He X, Gao J. Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In: Computer vision\u2013ECCV 2016: 14th European conference, Amsterdam, Proceedings, Part III 14, Springer, 2016; pp. 87\u2013102.","DOI":"10.1007\/978-3-319-46487-9_6"},{"key":"518_CR13","unstructured":"Yi D, Lei Z, Liao S, Li SZ. Learning face representation from scratch, arXiv preprint arXiv:1411.7923 (2014)."},{"key":"518_CR14","first-page":"2016","volume":"679","author":"P Regulation","year":"2016","unstructured":"Regulation P. Regulation (eu) 2016\/679 of the european parliament and of the council. Regulation (eu). 2016;679:2016.","journal-title":"Regulation (eu)"},{"key":"518_CR15","doi-asserted-by":"crossref","unstructured":"Joshi I, Grimmer M, Rathgeb C, Busch C, Bremond F, Dantcheva A, Synthetic data in human analysis: a survey, IEEE transactions on pattern analysis and machine intelligence (2024).","DOI":"10.1109\/TPAMI.2024.3362821"},{"key":"518_CR16","doi-asserted-by":"publisher","first-page":"102322","DOI":"10.1016\/j.inffus.2024.102322","volume":"107","author":"P Melzi","year":"2024","unstructured":"Melzi P, Tolosana R, Vera-Rodriguez R, Kim M, Rathgeb C, Liu X, et al. Frcsyn-ongoing: benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems. Inf Fus. 2024;107:102322.","journal-title":"Inf Fus"},{"key":"518_CR17","unstructured":"DeAndres-Tame I, Tolosana R, Melzi P, Vera-Rodriguez R, Kim M, Rathgeb C, et al. Face recognition challenge in the era of synthetic data. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2024;2024:3173\u201383."},{"issue":"11","key":"518_CR18","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks. Commun ACM. 2020;63(11):139\u201344.","journal-title":"Commun ACM"},{"key":"518_CR19","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. Adv Neural Inf Process Syst. 2020;33:6840\u201351.","journal-title":"Adv Neural Inf Process Syst"},{"key":"518_CR20","doi-asserted-by":"crossref","unstructured":"Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B. High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2022; pp. 10684\u201310695.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"518_CR21","doi-asserted-by":"crossref","unstructured":"Qiu H, Yu B, Gong D, Li Z, Liu W, Tao D. Synface: Face recognition with synthetic data. In: Proceedings of the IEEE\/CVF international conference on computer vision, 2021; pp. 10880\u201310890.","DOI":"10.1109\/ICCV48922.2021.01070"},{"key":"518_CR22","doi-asserted-by":"crossref","unstructured":"Bae G, de\u00a0La\u00a0Gorce M, Baltru\u0161aitis T, Hewitt C, Chen D, Valentin J, Cipolla R, Shen J. Digiface-1m: 1 million digital face images for face recognition. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, 2023; pp. 3526\u20133535.","DOI":"10.1109\/WACV56688.2023.00352"},{"key":"518_CR23","doi-asserted-by":"crossref","unstructured":"Kim M, Liu F, Jain A, Liu X. Dcface: Synthetic face generation with dual condition diffusion model. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2023; pp. 12715\u201312725.","DOI":"10.1109\/CVPR52729.2023.01223"},{"key":"518_CR24","doi-asserted-by":"crossref","unstructured":"Melzi P, Rathgeb C, Tolosana R, Vera-Rodriguez R, Lawatsch D, Domin F, Schaubert M. Gandiffface: Controllable generation of synthetic datasets for face recognition with realistic variations. In: Proceedings of the IEEE\/CVF international conference on computer vision, 2023; pp. 3086\u20133095.","DOI":"10.1109\/ICCVW60793.2023.00333"},{"key":"518_CR25","doi-asserted-by":"crossref","unstructured":"Papantoniou FP, Lattas A, Moschoglou S, Deng J, Kainz B, Zafeiriou S. Arc2face: A foundation model of human faces, arXiv preprint arXiv:2403.11641 (2024).","DOI":"10.1007\/978-3-031-72913-3_14"},{"key":"518_CR26","doi-asserted-by":"crossref","unstructured":"Boutros F, Grebe JH, Kuijper A, Damer N. Idiff-face: Synthetic-based face recognition through fizzy identity-conditioned diffusion model. In: Proceedings of the IEEE\/CVF international conference on computer vision, 2023; pp. 19650\u201319661.","DOI":"10.1109\/ICCV51070.2023.01800"},{"key":"518_CR27","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2019; pp. 4401\u20134410.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"518_CR28","unstructured":"Song J, Meng C, Ermon S. Denoising diffusion implicit models (2022). arXiv:2010.02502."},{"issue":"9","key":"518_CR29","doi-asserted-by":"publisher","first-page":"10850","DOI":"10.1109\/TPAMI.2023.3261988","volume":"45","author":"F-A Croitoru","year":"2023","unstructured":"Croitoru F-A, Hondru V, Ionescu RT, Shah M. Diffusion models in vision: a survey. IEEE Trans Pattern Anal Mach Intell. 2023;45(9):10850\u201369.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"518_CR30","doi-asserted-by":"crossref","unstructured":"Preechakul K, Chatthee N, Wizadwongsa S, Suwajanakorn S. Diffusion autoencoders: toward a meaningful and decodable representation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2022;10619\u201329.","DOI":"10.1109\/CVPR52688.2022.01036"},{"key":"518_CR31","first-page":"16222","volume":"36","author":"D Epstein","year":"2023","unstructured":"Epstein D, Jabri A, Poole B, Efros A, Holynski A. Diffusion self-guidance for controllable image generation. Adv Neural Inf Process Syst. 2023;36:16222\u201339.","journal-title":"Adv Neural Inf Process Syst"},{"key":"518_CR32","doi-asserted-by":"crossref","unstructured":"Wu CH, De\u00a0la Torre F. A latent space of stochastic diffusion models for zero-shot image editing and guidance. In: Proceedings of the IEEE\/CVF international conference on computer vision, 2023; pp. 7378\u20137387.","DOI":"10.1109\/ICCV51070.2023.00678"},{"key":"518_CR33","doi-asserted-by":"crossref","unstructured":"Blattmann A, Rombach R, Ling H, Dockhorn T, Kim SW, Fidler S, Kreis K. Align your latents: High-resolution video synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2023; pp. 22563\u201322575.","DOI":"10.1109\/CVPR52729.2023.02161"},{"key":"518_CR34","doi-asserted-by":"crossref","unstructured":"Ni H, Shi C, Li K, Huang SX, Min MR. Conditional image-to-video generation with latent flow diffusion models. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2023; pp. 18444\u201318455.","DOI":"10.1109\/CVPR52729.2023.01769"},{"key":"518_CR35","doi-asserted-by":"crossref","unstructured":"Zhu Y, Cao J, Liu B, Chen T, Xie R, Song L, et al. IEEE international symposium on broadband multimedia systems and broadcasting (BMSB). IEEE. 2024;2024:1\u20136.","DOI":"10.1109\/BMSB62888.2024.10608204"},{"key":"518_CR36","doi-asserted-by":"publisher","first-page":"100285","DOI":"10.1016\/j.cosrev.2020.100285","volume":"38","author":"G Harshvardhan","year":"2020","unstructured":"Harshvardhan G, Gourisaria MK, Pandey M, Rautaray SS. A comprehensive survey and analysis of generative models in machine learning. Comput Sci Rev. 2020;38:100285.","journal-title":"Comput Sci Rev"},{"key":"518_CR37","unstructured":"Cao P, Zhou F, Song Q, Yang L. Controllable generation with text-to-image diffusion models: a survey, arXiv preprint arXiv:2403.04279 (2024)."},{"key":"518_CR38","unstructured":"Zhang Y, Minhao L, Chen Z, Wu B, Zhan C, He Y, HUANG J, Zhou W, et\u00a0al. Musetalk: real-time high quality lip synchronization with latent space inpainting (2024)."},{"key":"518_CR39","unstructured":"Guo J, Zhang D, Liu X, Zhong Z, Zhang Y, Wan P, Zhang D. Liveportrait: efficient portrait animation with stitching and retargeting control, arXiv preprint arXiv:2407.03168 (2024)."},{"key":"518_CR40","unstructured":"Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S. Gans trained by a two time-scale update rule converge to a local nash equilibrium, In: Advances in neural information processing systems 30 (2017)."},{"key":"518_CR41","doi-asserted-by":"crossref","unstructured":"Zheng G, Zhou X, Li X, Qi Z, Shan Y, Li X. Layoutdiffusion: Controllable diffusion model for layout-to-image generation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2023; pp. 22490\u201322499.","DOI":"10.1109\/CVPR52729.2023.02154"},{"key":"518_CR42","doi-asserted-by":"crossref","unstructured":"Mokady R, Hertz A, Aberman K, Pritch Y, Cohen-Or D. Null-text inversion for editing real images using guided diffusion models. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2023; pp. 6038\u20136047.","DOI":"10.1109\/CVPR52729.2023.00585"},{"key":"518_CR43","doi-asserted-by":"crossref","unstructured":"Huberman-Spiegelglas I, Kulikov V, Michaeli T. An edit friendly ddpm noise space: Inversion and manipulations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2024; pp. 12469\u201312478.","DOI":"10.1109\/CVPR52733.2024.01185"},{"key":"518_CR44","doi-asserted-by":"crossref","unstructured":"Pan Z, Gherardi R, Xie X, Huang S. Effective real image editing with accelerated iterative diffusion inversion. In: Proceedings of the IEEE\/CVF international conference on computer vision. 2023;15912\u201321.","DOI":"10.1109\/ICCV51070.2023.01458"},{"key":"518_CR45","doi-asserted-by":"crossref","unstructured":"Kim G, Kwon T, Ye JC. Diffusionclip: text-guided diffusion models for robust image manipulation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2022; 2426\u201335.","DOI":"10.1109\/CVPR52688.2022.00246"},{"key":"518_CR46","doi-asserted-by":"publisher","first-page":"104688","DOI":"10.1016\/j.imavis.2023.104688","volume":"135","author":"F Boutros","year":"2023","unstructured":"Boutros F, Struc V, Fierrez J, Damer N. Synthetic data for face recognition: current state and future prospects. Image Vis Comput. 2023;135:104688.","journal-title":"Image Vis Comput"},{"key":"518_CR47","doi-asserted-by":"crossref","unstructured":"Boutros F, Huber M, Siebke P, Rieber T, Damer N. Sface: Privacy-friendly and accurate face recognition using synthetic data. In: 2022 IEEE International joint conference on biometrics (IJCB), IEEE, 2022; pp. 1\u201311.","DOI":"10.1109\/IJCB54206.2022.10007961"},{"key":"518_CR48","doi-asserted-by":"crossref","unstructured":"Kolf JN, Rieber T, Elliesen J, Boutros F, Kuijper A, Damer N. Identity-driven three-player generative adversarial network for synthetic-based face recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2023;806\u2013816.","DOI":"10.1109\/CVPRW59228.2023.00088"},{"key":"518_CR49","unstructured":"Li S, Xu J, Wu J, Xiong M, Deng A, Ji J, Huang Y, Feng W, Ding S, Hooi B. Id3: Identity-preserving-yet-diversified diffusion models for synthetic face recognition, arXiv preprint arXiv:2409.17576 (2024)."},{"key":"518_CR50","unstructured":"Sun Z, Song S, Patras I, Tzimiropoulos G. Cemiface: center-based semi-hard synthetic face generation for face recognition, arXiv preprint arXiv:2409.18876 (2024)."},{"key":"518_CR51","unstructured":"Oquab M, Darcet T, Moutakanni T, Vo H, Szafraniec M, Khalidov V, Fernandez P, Haziza D, Massa F, El-Nouby A, et\u00a0al. Dinov2: learning robust visual features without supervision, arXiv preprint arXiv:2304.07193 (2023)."},{"key":"518_CR52","doi-asserted-by":"crossref","unstructured":"Ou F-Z, Chen X, Zhang R, Huang Y, Li S, Li J, Li Y, Cao L, Wang Y-G. Sdd-fiqa: Unsupervised face image quality assessment with similarity distribution distance. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2021, pp. 7670\u20137679.","DOI":"10.1109\/CVPR46437.2021.00758"},{"key":"518_CR53","doi-asserted-by":"publisher","first-page":"103099","DOI":"10.1016\/j.inffus.2025.103099","volume":"120","author":"I DeAndres-Tame","year":"2025","unstructured":"DeAndres-Tame I, Tolosana R, Melzi P, Vera-Rodriguez R, Kim M, Rathgeb C, et al. Second frcsyn-ongoing: winning solutions and post-challenge analysis to improve face recognition with synthetic data. Inf Fus. 2025;120:103099.","journal-title":"Inf Fus"},{"key":"518_CR54","unstructured":"Kingma DP. Adam: a method for stochastic optimization, arXiv preprint arXiv:1412.6980 (2014)."},{"key":"518_CR55","unstructured":"Ruder S. An overview of gradient descent optimization algorithms, arXiv preprint arXiv:1609.04747 (2016)."},{"key":"518_CR56","unstructured":"Huang GB, Learned-Miller E. Labeled faces in the wild: updates and new reporting procedures, Tech. Rep. UM-CS-2014-003, University of Massachusetts, Amherst (May 2014)."},{"key":"518_CR57","doi-asserted-by":"crossref","unstructured":"Sengupta S, Chen J-C, Castillo C, Patel VM, Chellappa R, Jacobs DW, et al. IEEE winter conference on applications of computer vision (WACV). IEEE. 2016;2016:1\u20139.","DOI":"10.1109\/WACV.2016.7477558"},{"key":"518_CR58","doi-asserted-by":"crossref","unstructured":"Moschoglou S, Papaioannou A, Sagonas C, Deng J, Kotsia I, Zafeiriou S. Agedb: the first manually collected, in-the-wild age database. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017;51\u201359.","DOI":"10.1109\/CVPRW.2017.250"},{"issue":"7","key":"518_CR59","first-page":"5","volume":"5","author":"T Zheng","year":"2018","unstructured":"Zheng T, Deng W. Cross-pose lfw: a database for studying cross-pose face recognition in unconstrained environments, beijing university of posts and telecommunications. Tech Rep. 2018;5(7):5.","journal-title":"Tech Rep"},{"key":"518_CR60","unstructured":"Zheng T, Deng W, Hu J. Cross-age lfw: a database for studying cross-age face recognition in unconstrained environments, arXiv preprint arXiv:1708.08197 (2017)."},{"key":"518_CR61","doi-asserted-by":"crossref","unstructured":"Whitelam C, Taborsky E, Blanton A, Maze B, Adams J, Miller T, Kalka N, Jain AK, Duncan JA, Allen K, et\u00a0al. Iarpa janus benchmark-b face dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017;90\u201398.","DOI":"10.1109\/CVPRW.2017.87"},{"key":"518_CR62","doi-asserted-by":"crossref","unstructured":"Maze B, Adams J, Duncan JA, Kalka N, Miller T, Otto C, et al. International conference on biometrics (ICB). IEEE. 2018;2018:158\u201365.","DOI":"10.1109\/ICB2018.2018.00033"},{"key":"518_CR63","doi-asserted-by":"crossref","unstructured":"Boutros F, Klemt M, Fang M, Kuijper A, Damer N. Exfacegan: Exploring identity directions in gan\u2019s learned latent space for synthetic identity generation. In: 2023 IEEE international joint conference on biometrics (IJCB), IEEE, 2023;1\u201310.","DOI":"10.1109\/IJCB57857.2023.10449036"},{"key":"518_CR64","doi-asserted-by":"crossref","unstructured":"Boutros F, Huber M, Luu AT, Siebke P, Damer N. Sface2: Synthetic-based face recognition with w-space identity-driven sampling. IEEE transactions on biometrics: behavior, and identity science; 2024.","DOI":"10.1109\/TBIOM.2024.3371502"},{"key":"518_CR65","unstructured":"Kynk\u00e4\u00e4nniemi T, Karras T, Laine S, Lehtinen J, Aila T. Improved precision and recall metric for assessing generative models, Advances in neural information processing systems 32 (2019)."},{"key":"518_CR66","unstructured":"Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X. Improved techniques for training gans, Advances in neural information processing systems 29 (2016)."},{"key":"518_CR67","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L, et al. IEEE conference on computer vision and pattern recognition. IEEE. 2009;2009:248\u201355."},{"issue":"1","key":"518_CR68","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/TTS.2021.3111823","volume":"3","author":"P Terh\u00f6rst","year":"2021","unstructured":"Terh\u00f6rst P, Kolf JN, Huber M, Kirchbuchner F, Damer N, Moreno AM, et al. A comprehensive study on face recognition biases beyond demographics. IEEE Trans Technol Soc. 2021;3(1):16\u201330.","journal-title":"IEEE Trans Technol Soc"},{"issue":"11","key":"518_CR69","first-page":"8433","volume":"44","author":"M Wang","year":"2021","unstructured":"Wang M, Zhang Y, Deng W. Meta balanced network for fair face recognition. IEEE Trans Pattern Anal Mach Intell. 2021;44(11):8433\u201348.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"518_CR70","doi-asserted-by":"crossref","unstructured":"Wang M, Deng W. Mitigating bias in face recognition using skewness-aware reinforcement learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2020; pp. 9322\u20139331.","DOI":"10.1109\/CVPR42600.2020.00934"},{"key":"518_CR71","unstructured":"Kaplan J, McCandlish S, Henighan T, et\u00a0al. Scaling laws for neural language models, arXiv preprint arXiv:2001.08361 (2020)."},{"key":"518_CR72","unstructured":"Henighan T, Kaplan J, Katz M, et\u00a0al. Scaling laws for autoregressive generative modeling, arXiv preprint arXiv:2010.14701 (2020)."},{"key":"518_CR73","unstructured":"Hoffmann J, et\u00a0al. Training compute-optimal large language models, arXiv preprint arXiv:2203.15556 (2022)."},{"key":"518_CR74","unstructured":"Hernandez D, et\u00a0al. Scaling laws and interpretability of neural networks, arXiv preprint arXiv:2112.11446 (2021)."},{"key":"518_CR75","unstructured":"Fedus W, Zoph B, Shazeer N. Switch transformers: scaling to trillion parameter models with simple and efficient sparsity, arXiv preprint arXiv:2101.03961 (2021)."},{"key":"518_CR76","unstructured":"OpenAI, Gpt-4 technical report, arXiv preprint arXiv:2303.08774 (2023)."},{"key":"518_CR77","doi-asserted-by":"crossref","unstructured":"Narayanan D, et\u00a0al. Efficient large-scale language model training on gpu clusters, arXiv preprint arXiv:2104.04473 (2021).","DOI":"10.1145\/3458817.3476209"},{"key":"518_CR78","doi-asserted-by":"crossref","unstructured":"Chen S, Huang X, Zhong Z, Guan J, Zhou S. A focused human body model for accurate anthropometric measurements extraction. In: Proceedings of the computer vision and pattern recognition conference, 2025;22658\u201322667.","DOI":"10.1109\/CVPR52734.2025.02110"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00518-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00518-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00518-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T20:02:30Z","timestamp":1761163350000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00518-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,22]]},"references-count":78,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["518"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00518-z","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,22]]},"assertion":[{"value":"28 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We affirm that all data utilized in this paper are sourced from open-source licensed datasets\u00a0[\n                      \n                      ,\n                      \n                      ], which have been extensively employed in thousands of published papers and pose no potential ethical concerns. We confirm that all data used in this paper are from datasets\u00a0[\n                      \n                      ,\n                      \n                      ] that have obtained consent from participants and have been widely used in thousands of published papers.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"283"}}