{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T16:57:49Z","timestamp":1772989069851,"version":"3.50.1"},"reference-count":44,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"content-version":"vor","delay-in-days":47,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100015767","name":"Universitas Syiah Kuala","doi-asserted-by":"publisher","award":["487\/UN11\/SPK\/PNBP\/2022"],"award-info":[{"award-number":["487\/UN11\/SPK\/PNBP\/2022"]}],"id":[{"id":"10.13039\/501100015767","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>Face recognition is a reliable biometric technology that utilizes artificial intelligence and computer vision to identify and verify an individual\u2019s identity based on facial features. Its rapid development has enabled integration into diverse applications, including security systems, access control, forensics, and mobile devices. However, challenges such as recognizing faces across different ages persist, presenting opportunities for further research. Developing robust face recognition models requires substantial training datasets, but manually labeling large collections of facial images is both time\u2010intensive and costly. Our previous study demonstrated that a ResNet\u201050 model trained on a dataset of 44,000 facial images, combining the original FaceScrub dataset with HyperStyle age and smile augmentations, achieved an F1\u2010score of 79%, significantly outperforming the model trained solely on the authentic FaceScrub dataset (F1\u2010score: 63%). Building upon these findings, this study explores a broader range of augmentation styles, including pose and domain adaptation, e.g., Pixar, Toonify, Sketch, and Disney, using the HyperStyle scheme. Models trained with domain adaptation showed reduced performance (F1\u2010score: 64%) due to the domain gap introduced by cartoon\u2010like styles. In contrast, models trained with pose augmentation achieved an F1\u2010score of 75%, demonstrating the importance of augmentations resembling real\u2010world variations. When training incorporated all augmentation styles, e.g., age, smile, pose, and domain adaptation, the F1\u2010score reached 78%, slightly lower than the model trained with age and smile augmentations only (F1\u2010score: 79%). To further evaluate the impact of age and smile augmentations, additional experiments were conducted using various CNN models, including VGGNet\u201016, MobileNetV3Small, SEResNet\u201018, and ResNet\u201050, under different hyperparameter configurations. Among these, ResNet\u201050 achieved the highest F1\u2010score (82%), surpassing VGGNet\u201016 (60%), MobileNetV3Small (65%), and SEResNet\u201018 (81%). The study also introduced modifications to the HyperStyle scheme in both the preprocessing and postprocessing stages, which further enhanced model performance. The ResNet\u201050 model trained with the modified HyperStyle scheme, and the original FaceScrub dataset achieved the highest F1\u2010score of 83%, demonstrating the effectiveness of these modifications in improving face recognition performance.<\/jats:p>","DOI":"10.1155\/acis\/4097213","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T07:33:31Z","timestamp":1739777611000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Face Recognition Model Performance Through HyperStyle\u2010Driven Data Augmentation"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9441-9456","authenticated-orcid":false,"given":"Muhammad","family":"Chaidir","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3859-6706","authenticated-orcid":false,"given":"Taufik F.","family":"Abidin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0763-5610","authenticated-orcid":false,"given":"Hizir","family":"Sofyan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5740-1938","authenticated-orcid":false,"given":"Kahlil","family":"Muchtar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-021-00592-x"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.10.081"},{"key":"e_1_2_9_3_2","volume-title":"Advances in Neural Information Processing Systems","author":"Zhu Z.","year":"2014"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.10.427"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-021-00815-1"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46454-1_35"},{"key":"e_1_2_9_7_2","doi-asserted-by":"crossref","unstructured":"RahmatullahP. AbidinT. F. MisbullahA. andNazaruddin Effectiveness of Data Augmentation in Multi-Class Face Recognition Proceedings of the 5th International Conference on Informatics and Computational Sciences (ICICoS) June 2021 64\u201368 https:\/\/doi.org\/10.1109\/icicos53627.2021.9651780.","DOI":"10.1109\/ICICoS53627.2021.9651780"},{"key":"e_1_2_9_8_2","first-page":"2533","article-title":"A Survey on Various Problems and Challenges in Face Recognition","volume":"2","author":"Ohlyan S.","year":"2013","journal-title":"International Journal of Engineering Research and Technology"},{"key":"e_1_2_9_9_2","doi-asserted-by":"crossref","unstructured":"TaigmanY. YangM. RanzatoM. andWolfL. DeepFace: Closing the Gap to Human-Level Performance in Face Verification Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition May 2014 1701\u20131708.","DOI":"10.1109\/CVPR.2014.220"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.21512\/commit.v11i1.1847"},{"key":"e_1_2_9_11_2","doi-asserted-by":"crossref","unstructured":"HassnerT. HarelS. PazE. andEnbarR. Effective Face Frontalization in Unconstrained Images Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition June 2015 4295\u20134304 https:\/\/doi.org\/10.1109\/cvpr.2015.7299058 2-s2.0-84942466590.","DOI":"10.1109\/CVPR.2015.7299058"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2020.2970919"},{"key":"e_1_2_9_13_2","doi-asserted-by":"crossref","unstructured":"AlalufY. TovO. MokadyR. GalR. andBermanoA. HyperStyle: StyleGAN Inversion With Hypernetworks for Real Image Editing Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) August 2022 18511\u201318521.","DOI":"10.1109\/CVPR52688.2022.01796"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-020-02031-z"},{"key":"e_1_2_9_15_2","unstructured":"SimonyanK.andZissermanA. Very Deep Convolutional Networks for Large-Scale Image Recognition Proceedings of the 3rd International Conference on Learning Representations (ICLR) July 2015 1\u201314."},{"key":"e_1_2_9_16_2","doi-asserted-by":"crossref","unstructured":"HowardA. SandlerM. ChenB.et al. Searching for MobileNetV3 Proceedings of the IEEE\/CVF International Conference on Computer Vision January 2019 1314\u20131324 https:\/\/doi.org\/10.1109\/iccv.2019.00140.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2019.2913372"},{"key":"e_1_2_9_18_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. andSunJ. Deep Residual Learning for Image Recognition Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition March 2016 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/msp.2017.2764116"},{"key":"e_1_2_9_20_2","doi-asserted-by":"crossref","unstructured":"GhaziM. M.andEkenelH. K. A Comprehensive Analysis of Deep Learning-Based Representation for Face Recognition Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops February 2016 102\u2013109 https:\/\/doi.org\/10.1109\/cvprw.2016.20 2-s2.0-85010216414.","DOI":"10.1109\/CVPRW.2016.20"},{"key":"e_1_2_9_21_2","first-page":"1097","article-title":"ImageNet Classification With Deep Convolutional Neural Networks","author":"Krizhevsky A.","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_22_2","doi-asserted-by":"crossref","unstructured":"YangH.andPatrasI. Mirror Mirror on the Wall Tell Me Is the Error Small? Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) December 2015 4685\u20134693 https:\/\/doi.org\/10.1109\/cvpr.2015.7299100 2-s2.0-84959182127.","DOI":"10.1109\/CVPR.2015.7299100"},{"key":"e_1_2_9_23_2","doi-asserted-by":"crossref","unstructured":"XieS.andTuZ. Holistically-Nested Edge Detection Proceedings of the IEEE International Conference on Computer Vision May 2015 3\u201318 https:\/\/doi.org\/10.1007\/s11263-017-1004-z 2-s2.0-85015188516.","DOI":"10.1007\/s11263-017-1004-z"},{"key":"e_1_2_9_24_2","first-page":"1988","article-title":"Deep Learning Face Representation by Joint Identification-Verification","volume":"3","author":"Sun Y.","year":"2014","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_25_2","article-title":"Targeting Ultimate Accuracy: Face Recognition via Deep Embedding","author":"Liu J.","year":"2015","journal-title":"arXiv preprint arXiv:1506.07310"},{"key":"e_1_2_9_26_2","doi-asserted-by":"crossref","unstructured":"MasiI. HassnerT. Tr\u00e0nA. T. andMedioniG. Rapid Synthesis of Massive Face Sets for Improved Face Recognition Proceedings of the 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017) June 2017 604\u2013611 https:\/\/doi.org\/10.1109\/fg.2017.76 2-s2.0-85026323751.","DOI":"10.1109\/FG.2017.76"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"e_1_2_9_28_2","article-title":"Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis","volume":"30","author":"Zhao J.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_29_2","unstructured":"KangG. DongX. ZhengL. andYangY. PatchShuffle Regularization 2017."},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459838"},{"key":"e_1_2_9_32_2","doi-asserted-by":"crossref","unstructured":"AlalufY. PatashnikO. andCohen-OrD. ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) September 2021 6711\u20136720.","DOI":"10.1109\/ICCV48922.2021.00664"},{"key":"e_1_2_9_33_2","doi-asserted-by":"crossref","unstructured":"NgH.-W.andWinklerS. A Data-Driven Approach to Cleaning Large Face Datasets Proceedings of the IEEE International Conference on Image Processing March 2014 343\u2013347 https:\/\/doi.org\/10.1109\/icip.2014.7025068 2-s2.0-84943293001.","DOI":"10.1109\/ICIP.2014.7025068"},{"key":"e_1_2_9_34_2","doi-asserted-by":"crossref","unstructured":"ChaidirM. AbidinT. F. andMuchtarK. HyperStyle-Based Data Augmentation to Improve the Performance of Face Recognition Model Proceedings of the International Conference on Electrical Engineering and Informatics (ICELTICs) May 2022 49\u201354 https:\/\/doi.org\/10.1109\/iceltics56128.2022.9932083.","DOI":"10.1109\/ICELTICs56128.2022.9932083"},{"key":"e_1_2_9_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/lsp.2016.2603342"},{"key":"e_1_2_9_36_2","doi-asserted-by":"crossref","unstructured":"WangH. BhaskaraV. LevinshteinA. TsogkasS. andJepsonA. Efficient Super-Resolution Using MobileNetV3 Proceedings of the Computer Vision \u2013 ECCV Workshops June 2020 87\u2013102 https:\/\/doi.org\/10.1007\/978-3-030-67070-2_5.","DOI":"10.1007\/978-3-030-67070-2_5"},{"key":"e_1_2_9_37_2","unstructured":"HowardA. G. ZhuM. ChenB.et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications 2017."},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.56294\/dm2023153"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2022.100128"},{"key":"e_1_2_9_40_2","doi-asserted-by":"publisher","DOI":"10.3390\/app10020557"},{"key":"e_1_2_9_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2020.102961"},{"key":"e_1_2_9_42_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-021-09587-6"},{"key":"e_1_2_9_43_2","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2019.00083"},{"key":"e_1_2_9_44_2","doi-asserted-by":"crossref","unstructured":"KobayashiK. TsujiJ. andNotoM. Evaluation of Data Augmentation for Image-Based Plant-Disease Detection Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC) April 2018 2206\u20132211 https:\/\/doi.org\/10.1109\/smc.2018.00379 2-s2.0-85062233087.","DOI":"10.1109\/SMC.2018.00379"}],"container-title":["Applied Computational Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/acis\/4097213","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/acis\/4097213","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/acis\/4097213","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T11:46:48Z","timestamp":1772970408000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/acis\/4097213"}},"subtitle":[],"editor":[{"given":"Dimitrios A.","family":"Karras","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1155\/acis\/4097213"],"URL":"https:\/\/doi.org\/10.1155\/acis\/4097213","archive":["Portico"],"relation":{},"ISSN":["1687-9724","1687-9732"],"issn-type":[{"value":"1687-9724","type":"print"},{"value":"1687-9732","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2024-05-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"4097213"}}