{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:15:56Z","timestamp":1758845756596,"version":"3.44.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s41060-024-00683-x","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T01:54:43Z","timestamp":1737510883000},"page":"3595-3612","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Facial attribute manipulation using the contrastive disentangled generative adversarial network framework with 3D priors"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8542-1351","authenticated-orcid":false,"given":"Wiem","family":"Grina","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0178-501X","authenticated-orcid":false,"given":"Ali","family":"Douik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,22]]},"reference":[{"key":"683_CR1","doi-asserted-by":"publisher","first-page":"56292","DOI":"10.1109\/ACCESS.2021.3072057","volume":"9","author":"D Li","year":"2021","unstructured":"Li, D., Qi, W., Sun, S.: Facial landmarks and expression label guided photorealistic facial expression synthesis. IEEE Access 9, 56292\u201356300 (2021)","journal-title":"IEEE Access"},{"key":"683_CR2","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s00371-020-01794-9","volume":"37","author":"B Fredj","year":"2021","unstructured":"Fredj, B., Bouguezzi, H., Souani, C.: Face recognition in unconstrained environment with CNN. Vis. Comput. 37, 217\u2013226 (2021)","journal-title":"Vis. Comput."},{"key":"683_CR3","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA 14\u201319, pp. 8110\u20138119 (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"683_CR4","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, Salt Lake City, UT, USA, 18\u201323 June 2018, pp. 8789\u20138797 (2018)","DOI":"10.1109\/CVPR.2018.00916"},{"key":"#cr-split#-683_CR5.1","doi-asserted-by":"crossref","unstructured":"Wiem, G., Ali, D.: Automatic facial expression neutralisation using generative adversarial network. In: Proceedings of the International Conference on Engineering Applications of Neural Networks, Halkidiki, Greece, 25-27 June 2021","DOI":"10.1007\/978-3-030-80568-5_1"},{"key":"#cr-split#-683_CR5.2","unstructured":"Springer: Cham, Switzerland, pp. 3-12 (2021)"},{"key":"683_CR6","doi-asserted-by":"crossref","unstructured":"Yin, Y., Jiang, S., Robinson, J.P., Fu, Y.: Dual-attention GAN for large-pose face frontalization. In: Proceedings of the 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), Buenos Aires, Argentina, 16\u201320 November 2020, pp. 249\u2013256","DOI":"10.1109\/FG47880.2020.00004"},{"key":"683_CR7","doi-asserted-by":"crossref","unstructured":"Huang, R., Zhang, S., Li, T., He, R.: 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, Venice, Italy 22\u201329, pp. 2439\u20132448 (2017)","DOI":"10.1109\/ICCV.2017.267"},{"key":"683_CR8","unstructured":"Deng, Y., Yang, J., Chen, D., Wen, F., Tong, X.: Disentangled and controllable face image generation via 3d imitative-contrastive learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 14\u201319 June, pp. 5154\u20135163"},{"key":"683_CR9","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, Long Beach, CA, USA 15\u201320, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"683_CR10","unstructured":"Bendel, O.: Image synthesis from an ethical perspective. AI & SOCIETY, pp. 1\u201310 (2023)"},{"key":"683_CR11","doi-asserted-by":"crossref","unstructured":"Cao, M., Wang, X., Qi, Z., et al.: Masactrl: tuning-free mutual self-attention control for consistent image synthesis and editing. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 22560\u201322570 (2023)","DOI":"10.1109\/ICCV51070.2023.02062"},{"key":"683_CR12","doi-asserted-by":"crossref","unstructured":"Abdal, R., Zhu, P., Mitra, N.J., et al.: Styleflow: attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows. ACM Trans. Graph. (ToG), vol. 40, no 3, pp. 1\u201321 (2021)","DOI":"10.1145\/3447648"},{"key":"683_CR13","doi-asserted-by":"crossref","unstructured":"Deng, K., Yang, G., Ramanan, D., et al.: 3D-aware conditional image synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4434\u20134445 (2023)","DOI":"10.1109\/CVPR52729.2023.00431"},{"key":"683_CR14","unstructured":"Deng, Y., Yang, J., Xu, S., et al.: Accurate 3D face reconstruction with weakly-supervised learning: from single image to image set. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops"},{"key":"683_CR15","doi-asserted-by":"publisher","unstructured":"Hu, C.H., Liu, Y., Xu, L.T., Jing, X.Y., Lu, X.B., Yang, W.K., Liu, P.: Joint image-to-image translation for traffic monitoring driver face image enhancement. IEEE Trans. Intell. Transp. Syst. 24, 7961\u20137973 (2023). https:\/\/doi.org\/10.1109\/TITS.2022.3129859","DOI":"10.1109\/TITS.2022.3129859"},{"key":"683_CR16","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y., et al.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the ICML, Atlanta, GA, USA, 17\u201319 June 2013, p. 3 (2013)"},{"key":"683_CR17","doi-asserted-by":"crossref","unstructured":"Sun, J., Deng, Q., Li, Q., et al.: AnyFace++: a unified framework for free-style text-to-face synthesis and manipulation. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2023.3345866"},{"key":"683_CR18","doi-asserted-by":"publisher","first-page":"217","DOI":"10.0000\/viscom.2021.37.217-226","volume":"37","author":"B Fredj","year":"2021","unstructured":"Fredj, B., Bouguezzi, H., Souani, C.: Face recognition in unconstrained environment with CNN. Vis. Comput. 37, 217\u2013226 (2021). https:\/\/doi.org\/10.0000\/viscom.2021.37.217-226","journal-title":"Vis. Comput."},{"key":"683_CR19","doi-asserted-by":"crossref","unstructured":"Suwa\u0142a, A, W\u00f3jcik, B, Proszewska M et al.: Face identity-aware disentanglement in StyleGAN. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 5222\u20135231 (2024)","DOI":"10.1109\/WACV57701.2024.00514"},{"key":"683_CR20","doi-asserted-by":"crossref","unstructured":"Zhan, F., Yu, Yi., Wu, R., et al.: Multimodal image synthesis and editing: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. (2023)","DOI":"10.1109\/TPAMI.2023.3305243"},{"key":"683_CR21","doi-asserted-by":"crossref","unstructured":"Shi, J., Liu, W., Zhou, G., et al.: AutoInfo GAN: toward a better image synthesis GAN framework for high-fidelity few-shot datasets via NAS and contrastive learning. Knowl. Based Syst. vol. 276, p. 110757 (2023)","DOI":"10.1016\/j.knosys.2023.110757"},{"key":"683_CR22","unstructured":"Lin, Z., Thekumparampil, K., Fanti, G., Oh, S.: Infogan-cr and modelcentrality: self-supervised model training and selection for disentangling gans. In: Proceedings of the International Conference on Machine Learning, PMLR, Virtual 13\u201318, pp. 6127\u20136139 (2020)"},{"key":"683_CR23","unstructured":"Donahue, C., Lipton, Z.C., Balsubramani, A., McAuley, J.: Semantically decomposing the latent spaces of generative adversarial networks. arXiv2017. arXiv:1705.07904"},{"key":"683_CR24","doi-asserted-by":"crossref","unstructured":"Xu, W., Long, C., Nie, Y., et al.: Disentangled representation learning for controllable person image generation. IEEE Trans. Multimedia (2024)","DOI":"10.1109\/TMM.2023.3345180"},{"key":"683_CR25","doi-asserted-by":"crossref","unstructured":"Bai, Y., Fan, Y., Wang, X., Zhang, Y., Sun, J., Yuan, C., Shan, Y.: High-fidelity facial avatar reconstruction from monocular video with generative priors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 17\u201324 June 2023, pp. 4541\u20134551 (2023)","DOI":"10.1109\/CVPR52729.2023.00441"},{"key":"683_CR26","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Sun, L.: Disentangling latent space for vae by label relevant\/irrelevant dimensions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12192\u201312201 (2019)","DOI":"10.1109\/CVPR.2019.01247"},{"key":"683_CR27","doi-asserted-by":"crossref","unstructured":"Serengil, S.I., Ozpinar, A.: Lightface: a hybrid deep face recognition framework. In: Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE 2020, pp. 1\u20135 (2020)","DOI":"10.1109\/ASYU50717.2020.9259802"},{"key":"683_CR28","doi-asserted-by":"crossref","unstructured":"Wu, H., Jia, J., Xie, L., et al.: Cross-VAE: Towards disentangling expression from identity for human faces. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. pp. 4087\u20134091","DOI":"10.1109\/ICASSP40776.2020.9053608"},{"key":"683_CR29","doi-asserted-by":"crossref","unstructured":"Li, P., Huang, H., Hu, Y., et al.: Hierarchical face aging through disentangled latent characteristics. In: Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part III 16. Springer International Publishing, pp. 86-101 (2020)","DOI":"10.1007\/978-3-030-58580-8_6"},{"key":"683_CR30","doi-asserted-by":"crossref","unstructured":"Zhang C, Wang C, Zhao Y et al. : DR$$^{2}$$: Disentangled recurrent representation learning for data-efficient speech video synthesis. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 2024. pp 6204\u20136214","DOI":"10.1109\/WACV57701.2024.00609"},{"key":"683_CR31","doi-asserted-by":"crossref","unstructured":"Guo, H., Ma, Z., Chen, X., et al.: Generating artistic portraits from face photos with feature disentanglement and reconstruction. Electronics 13(5), 955 (2024)","DOI":"10.3390\/electronics13050955"},{"key":"#cr-split#-683_CR32.1","doi-asserted-by":"crossref","unstructured":"Shen, Y., Luo, P., Yan, J., Wang, X., Tang, X.: 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, Salt Lake City, UT, USA, 18-23 June 2018","DOI":"10.1109\/CVPR.2018.00092"},{"key":"#cr-split#-683_CR32.2","unstructured":"pp. 821-830. Manipulation on the generative image manifold. arXiv preprint arXiv:2305.10973 (2023)"},{"key":"683_CR33","doi-asserted-by":"crossref","unstructured":"Tewari, A., Elgharib, M., Bernard, F., Seidel, H.P., P\u00e9rez, P., Zoll- 1292 h\u00f6fer,M., Theobalt, C.:StyleRig: Rigging StyleGAN for 3D control over portrait images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, pp 6142\u20136151 (2020)","DOI":"10.1109\/CVPR42600.2020.00618"},{"key":"683_CR34","doi-asserted-by":"crossref","unstructured":"Yao, X., Puy, G., Newson, A., Gousseau, Y., Hellier, P.: High resolution face age editing. In: Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 10\u201315 January 2021, pp. 8624\u20138631 (2021)","DOI":"10.1109\/ICPR48806.2021.9412383"},{"key":"683_CR35","doi-asserted-by":"crossref","unstructured":"Tran, L., Liu, X.: Nonlinear 3d face morphable model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA 18\u201323, pp. 7346\u20137355 (2018)","DOI":"10.1109\/CVPR.2018.00767"},{"key":"683_CR36","doi-asserted-by":"crossref","unstructured":"Jesus, H., Proen\u00e7a, H.: Towards zero-shot interpretable human recognition: a 2D-3D registration framework. arXiv preprint arXiv:2403.06658 (2024)","DOI":"10.1109\/IJCB62174.2024.10744516"},{"key":"683_CR37","doi-asserted-by":"crossref","unstructured":"Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., Yang, Y.L.: Hologan: unsupervised learning of 3D representations from natural images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 27 October\u20132 November 2019, pp. 7588\u20137597 (2019)","DOI":"10.1109\/ICCV.2019.00768"},{"key":"683_CR38","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y., et al.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the ICML, Atlanta, GA, USA, 17\u201319 June 2013; p. 3 (2013)"},{"key":"683_CR39","unstructured":"Liu, Z., Luo, P., Wang, X., et al.: Large-scale celebfaces attributes (celeba) dataset. Retrieved August, vol. 15, p. 11 (2018)"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-024-00683-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-024-00683-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-024-00683-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T10:54:40Z","timestamp":1758797680000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-024-00683-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,22]]},"references-count":41,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["683"],"URL":"https:\/\/doi.org\/10.1007\/s41060-024-00683-x","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"type":"print","value":"2364-415X"},{"type":"electronic","value":"2364-4168"}],"subject":[],"published":{"date-parts":[[2025,1,22]]},"assertion":[{"value":"30 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 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":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This research draws upon the widely used Flickr-Faces-HQ (FFHQ) dataset, which has been extensively employed in various prior research studies. The FFHQ dataset is a high-quality image dataset of human faces, originally created as a benchmark for generative adversarial networks (GAN) is made available under Creative Commons BY-NC-SA 4.0 license by NVIDIA Corporation, is a publicly available dataset designed for research purposes and has gained recognition for its contributions to the field. It is important to emphasize that the FFHQ dataset contains images that were collected and shared under terms of use and distribution specified by its creators. As the dataset comprises images of individuals obtained from various sources, our study does not involve direct interaction with human subjects. Consequently, ethical considerations related to informed consent, privacy, and confidentiality do not apply to this research. We exclusively analyze the existing and de-identified image data to draw insights. We would like to acknowledge and appreciate the contributions of the creators of the FFHQ dataset. Proper citations to the dataset\u2019s source and any pertinent publications that facilitated its establishment are provided in the references section of this manuscript. This section clearly communicates that the research utilizes the FFHQ dataset, specifying its origin, purpose, and absence of direct human subject involvement. We ensure that our citations and acknowledgments accurately reflect the dataset\u2019s origin and any relevant contributions from its creators. Informed consent was obtained from all subjects involved in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}