{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T16:38:39Z","timestamp":1772642319880,"version":"3.50.1"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031730009","type":"print"},{"value":"9783031730016","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73001-6_10","type":"book-chapter","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T10:21:46Z","timestamp":1732616506000},"page":"163-182","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["AdaDistill: Adaptive Knowledge Distillation for\u00a0Deep Face Recognition"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4516-9128","authenticated-orcid":false,"given":"Fadi","family":"Boutros","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3385-5780","authenticated-orcid":false,"given":"Vitomir","family":"\u0160truc","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7910-7895","authenticated-orcid":false,"given":"Naser","family":"Damer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"10_CR1","unstructured":"Baruch, E.B., Karklinsky, M., Biton, Y., Ben-Cohen, A., Lawen, H., Zamir, N.: It\u2019s all in the head: representation knowledge distillation through classifier sharing. CoRR abs\/2201.06945 (2022). https:\/\/arxiv.org\/abs\/2201.06945"},{"key":"10_CR2","doi-asserted-by":"publisher","unstructured":"Boutros, F., Damer, N., Fang, M., Kirchbuchner, F., Kuijper, A.: MixFaceNets: extremely efficient face recognition networks. In: International IEEE Joint Conference on Biometrics, IJCB 2021, Shenzhen, China, 4\u20137 August 2021, pp.\u00a01\u20138. IEEE (2021). https:\/\/doi.org\/10.1109\/IJCB52358.2021.9484374","DOI":"10.1109\/IJCB52358.2021.9484374"},{"key":"10_CR3","doi-asserted-by":"publisher","unstructured":"Boutros, F., Damer, N., Kirchbuchner, F., Kuijper, A.: ElasticFace: elastic margin loss for deep face recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2022, New Orleans, LA, USA, 19\u201320 June 2022, pp. 1577\u20131586. IEEE (2022). https:\/\/doi.org\/10.1109\/CVPRW56347.2022.00164","DOI":"10.1109\/CVPRW56347.2022.00164"},{"key":"10_CR4","doi-asserted-by":"publisher","unstructured":"Boutros, F., Damer, N., Kuijper, A.: QuantFace: towards lightweight face recognition by synthetic data low-bit quantization. In: 26th International Conference on Pattern Recognition, ICPR 2022, Montreal, QC, Canada, 21\u201325 August 2022, pp. 855\u2013862. IEEE (2022). https:\/\/doi.org\/10.1109\/ICPR56361.2022.9955645","DOI":"10.1109\/ICPR56361.2022.9955645"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Boutros, F., Grebe, J.H., 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 (ICCV), pp. 19650\u201319661 (2023)","DOI":"10.1109\/ICCV51070.2023.01800"},{"key":"10_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2023.104688","volume":"135","author":"F Boutros","year":"2023","unstructured":"Boutros, F., Struc, V., Fi\u00e9rrez, J., Damer, N.: Synthetic data for face recognition: current state and future prospects. Image Vis. Comput. 135, 104688 (2023). https:\/\/doi.org\/10.1016\/j.imavis.2023.104688","journal-title":"Image Vis. Comput."},{"key":"10_CR7","doi-asserted-by":"publisher","unstructured":"Bucila, C., Caruana, R., Niculescu-Mizil, A.: Model compression. In: Eliassi-Rad, T., Ungar, L.H., Craven, M., Gunopulos, D. (eds.) Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, 20\u201323 August 2006, pp. 535\u2013541. ACM (2006). https:\/\/doi.org\/10.1145\/1150402.1150464","DOI":"10.1145\/1150402.1150464"},{"key":"10_CR8","doi-asserted-by":"publisher","unstructured":"Caldeira, E., Neto, P.C., Huber, M., Damer, N., Sequeira, A.F.: Model compression techniques in biometrics applications: a survey. CoRR abs\/2401.10139 (2024). https:\/\/doi.org\/10.48550\/ARXIV.2401.10139","DOI":"10.48550\/ARXIV.2401.10139"},{"key":"10_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1007\/978-3-319-97909-0_46","volume-title":"Biometric Recognition","author":"S Chen","year":"2018","unstructured":"Chen, S., Liu, Y., Gao, X., Han, Z.: MobileFaceNets: efficient CNNs for accurate real-time face verification on mobile devices. In: Zhou, J., et al. (eds.) CCBR 2018. LNCS, vol. 10996, pp. 428\u2013438. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-97909-0_46"},{"key":"10_CR10","unstructured":"Chen, Y., Wang, N., Zhang, Z.: DarkRank: accelerating deep metric learning via cross sample similarities transfer. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-2018), The 30th Innovative Applications of Artificial Intelligence (IAAI-2018), and The 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-2018), New Orleans, Louisiana, USA, 2\u20137 February 2018, pp. 2852\u20132859. AAAI Press (2018). https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/17147"},{"key":"10_CR11","doi-asserted-by":"publisher","unstructured":"Cho, J.H., Hariharan, B.: On the efficacy of knowledge distillation. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), 27 October\u20132 November 2019, pp. 4793\u20134801. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00489","DOI":"10.1109\/ICCV.2019.00489"},{"key":"10_CR12","doi-asserted-by":"publisher","unstructured":"Dan, J., et al.: TransFace: calibrating transformer training for face recognition from a data-centric perspective. In: IEEE\/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, 1\u20136 October 2023, pp. 20585\u201320596. IEEE (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.01887","DOI":"10.1109\/ICCV51070.2023.01887"},{"key":"10_CR13","doi-asserted-by":"publisher","unstructured":"Deng, J., Guo, J., An, X., Zhu, Z., Zafeiriou, S.: Masked face recognition challenge: the insightface track report. In: IEEE\/CVF International Conference on Computer Vision Workshops, ICCVW 2021, Montreal, BC, Canada, 11\u201317 October 2021, pp. 1437\u20131444. IEEE (2021). https:\/\/doi.org\/10.1109\/ICCVW54120.2021.00165","DOI":"10.1109\/ICCVW54120.2021.00165"},{"key":"10_CR14","doi-asserted-by":"publisher","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 4690\u20134699. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00482","DOI":"10.1109\/CVPR.2019.00482"},{"key":"10_CR15","doi-asserted-by":"publisher","unstructured":"Deng, J., Guo, J., Zhang, D., Deng, Y., Lu, X., Shi, S.: Lightweight face recognition challenge. In: 2019 IEEE\/CVF International Conference on Computer Vision Workshops, ICCV Workshops 2019, Seoul, Korea (South), 27\u201328 October 2019, pp. 2638\u20132646. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCVW.2019.00322","DOI":"10.1109\/ICCVW.2019.00322"},{"key":"10_CR16","unstructured":"Duong, C.N., Luu, K., Quach, K.G., Le, N.: ShrinkTeaNet: million-scale lightweight face recognition via shrinking teacher-student networks. CoRR abs\/1905.10620 (2019). http:\/\/arxiv.org\/abs\/1905.10620"},{"key":"10_CR17","doi-asserted-by":"publisher","unstructured":"Feng, Y., Wang, H., Hu, H.R., Yu, L., Wang, W., Wang, S.: Triplet distillation for deep face recognition. In: IEEE International Conference on Image Processing, ICIP 2020, Abu Dhabi, United Arab Emirates, 25\u201328 October 2020, pp. 808\u2013812. IEEE (2020). https:\/\/doi.org\/10.1109\/ICIP40778.2020.9190651","DOI":"10.1109\/ICIP40778.2020.9190651"},{"issue":"6","key":"10_CR18","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. Int. J. Comput. Vis. 129(6), 1789\u20131819 (2021). https:\/\/doi.org\/10.1007\/s11263-021-01453-z","journal-title":"Int. J. Comput. Vis."},{"key":"10_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-3-319-46487-9_6","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Y Guo","year":"2016","unstructured":"Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part III. LNCS, vol. 9907, pp. 87\u2013102. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_6"},{"key":"10_CR20","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27\u201330 June 2016, pp. 770\u2013778. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"10_CR21","unstructured":"Hinton, G.E., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. CoRR abs\/1503.02531 (2015). http:\/\/arxiv.org\/abs\/1503.02531. nIPS 2014 Deep Learning Workshop"},{"key":"10_CR22","unstructured":"Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07-49, University of Massachusetts, Amherst (2007)"},{"key":"10_CR23","doi-asserted-by":"publisher","unstructured":"Huang, Y., et al.: CurricularFace: adaptive curriculum learning loss for deep face recognition. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13\u201319 June 2020, pp. 5900\u20135909. Computer Vision Foundation\/IEEE (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00594. https:\/\/openaccess.thecvf.com\/content_CVPR_2020\/html\/Huang_CurricularFace_Adaptive_Curriculum_Learning_Loss_for_Deep_Face_Recognition_CVPR_2020_paper.html","DOI":"10.1109\/CVPR42600.2020.00594"},{"key":"10_CR24","doi-asserted-by":"publisher","unstructured":"Huang, Y., Wu, J., Xu, X., Ding, S.: Evaluation-oriented knowledge distillation for deep face recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, 18\u201324 June 2022, pp. 18719\u201318728. IEEE (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.01818","DOI":"10.1109\/CVPR52688.2022.01818"},{"key":"10_CR25","doi-asserted-by":"publisher","unstructured":"Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E.: The megaface benchmark: 1 million faces for recognition at scale. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27\u201330 June 2016, pp. 4873\u20134882. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.527","DOI":"10.1109\/CVPR.2016.527"},{"key":"10_CR26","doi-asserted-by":"publisher","unstructured":"Kim, M., Liu, F., Jain, A.K., Liu, X.: DCFace: synthetic face generation with dual condition diffusion model. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, 17\u201324 June 2023, pp. 12715\u201312725. IEEE (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.01223","DOI":"10.1109\/CVPR52729.2023.01223"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: Rethinking feature-based knowledge distillation for face recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20156\u201320165 (2023)","DOI":"10.1109\/CVPR52729.2023.01930"},{"key":"10_CR28","doi-asserted-by":"publisher","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: SphereFace: deep hypersphere embedding for face recognition. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21\u201326 July 2017, pp. 6738\u20136746. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.713","DOI":"10.1109\/CVPR.2017.713"},{"key":"10_CR29","doi-asserted-by":"publisher","unstructured":"Maze, B., et al.: IARPA Janus benchmark - C: face dataset and protocol. In: 2018 International Conference on Biometrics, ICB 2018, Gold Coast, Australia, 20\u201323 February 2018, pp. 158\u2013165. IEEE (2018). https:\/\/doi.org\/10.1109\/ICB2018.2018.00033","DOI":"10.1109\/ICB2018.2018.00033"},{"key":"10_CR30","doi-asserted-by":"publisher","unstructured":"Mirzadeh, S., Farajtabar, M., Li, A., Levine, N., Matsukawa, A., Ghasemzadeh, H.: Improved knowledge distillation via teacher assistant. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, 7\u201312 February 2020, pp. 5191\u20135198. AAAI Press (2020). https:\/\/doi.org\/10.1609\/AAAI.V34I04.5963","DOI":"10.1609\/AAAI.V34I04.5963"},{"key":"10_CR31","doi-asserted-by":"publisher","unstructured":"Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: AgeDB: the first manually collected, in-the-wild age database. In: 2017 IEEE CVPRW, CVPR Workshops 2017, Honolulu, HI, USA, 21\u201326 July 2017, pp. 1997\u20132005. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPRW.2017.250","DOI":"10.1109\/CVPRW.2017.250"},{"key":"10_CR32","unstructured":"Park, D.Y., Cha, M., Jeong, C., Kim, D., Han, B.: Learning student-friendly teacher networks for knowledge distillation. In: Ranzato, M., Beygelzimer, A., Dauphin, Y.N., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, 6\u201314 December 2021, virtual, pp. 13292\u201313303 (2021). https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/6e7d2da6d3953058db75714ac400b584-Abstract.html"},{"key":"10_CR33","doi-asserted-by":"publisher","unstructured":"Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 3967\u20133976. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00409. http:\/\/openaccess.thecvf.com\/content_CVPR_2019\/html\/Park_Relational_Knowledge_Distillation_CVPR_2019_paper.html","DOI":"10.1109\/CVPR.2019.00409"},{"key":"10_CR34","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024\u20138035. Curran Associates, Inc. (2019). http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"10_CR35","doi-asserted-by":"publisher","unstructured":"Peng, B., et al.: Correlation congruence for knowledge distillation. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), 27 October\u20132 November 2019, pp. 5006\u20135015. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00511","DOI":"10.1109\/ICCV.2019.00511"},{"key":"10_CR36","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: hints for thin deep nets. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015). http:\/\/arxiv.org\/abs\/1412.6550"},{"key":"10_CR37","unstructured":"Ruder, S.: An overview of gradient descent optimization algorithms. CoRR abs\/1609.04747 (2016). http:\/\/arxiv.org\/abs\/1609.04747"},{"key":"10_CR38","doi-asserted-by":"publisher","unstructured":"Sengupta, S., Chen, J., Castillo, C.D., Patel, V.M., Chellappa, R., Jacobs, D.W.: Frontal to profile face verification in the wild. In: 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016, Lake Placid, NY, USA, 7\u201310 March 2016, pp.\u00a01\u20139. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/WACV.2016.7477558","DOI":"10.1109\/WACV.2016.7477558"},{"key":"10_CR39","unstructured":"Svitov, D., Alyamkin, S.: MarginDistillation: distillation for margin-based softmax. CoRR abs\/2003.02586 (2020). https:\/\/arxiv.org\/abs\/2003.02586"},{"key":"10_CR40","doi-asserted-by":"publisher","unstructured":"Tung, F., Mori, G.: Similarity-preserving knowledge distillation. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), 27 October\u20132 November 2019, pp. 1365\u20131374. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00145","DOI":"10.1109\/ICCV.2019.00145"},{"key":"10_CR41","doi-asserted-by":"publisher","unstructured":"Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 5265\u20135274. IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00552","DOI":"10.1109\/CVPR.2018.00552"},{"issue":"6","key":"10_CR42","doi-asserted-by":"publisher","first-page":"3048","DOI":"10.1109\/TPAMI.2021.3055564","volume":"44","author":"L Wang","year":"2022","unstructured":"Wang, L., Yoon, K.: Knowledge distillation and student-teacher learning for visual intelligence: a review and new outlooks. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 3048\u20133068 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3055564","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR43","doi-asserted-by":"publisher","unstructured":"Whitelam, C., et al.: IARPA Janus benchmark-b face dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2017, Honolulu, HI, USA, 21\u201326 July 2017, pp. 592\u2013600. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPRW.2017.87","DOI":"10.1109\/CVPRW.2017.87"},{"key":"10_CR44","doi-asserted-by":"publisher","unstructured":"Yan, M., Zhao, M., Xu, Z., Zhang, Q., Wang, G., Su, Z.: VarGFaceNet: an efficient variable group convolutional neural network for lightweight face recognition. In: 2019 IEEE\/CVF International Conference on Computer Vision Workshops, ICCV Workshops 2019, Seoul, Korea (South), 27\u201328 October 2019, pp. 2647\u20132654. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCVW.2019.00323","DOI":"10.1109\/ICCVW.2019.00323"},{"key":"10_CR45","unstructured":"Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. CoRR abs\/1411.7923 (2014). http:\/\/arxiv.org\/abs\/1411.7923"},{"issue":"10","key":"10_CR46","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 23(10), 1499\u20131503 (2016)","journal-title":"IEEE Sig. Process. Lett."},{"key":"10_CR47","unstructured":"Zheng, T., Deng, W.: Cross-Pose LFW: a database for studying cross-pose face recognition in unconstrained environments. Technical report 18-01, Beijing University of Posts and Telecommunications (2018)"},{"key":"10_CR48","unstructured":"Zheng, T., Deng, W., Hu, J.: Cross-Age LFW: a database for studying cross-age face recognition in unconstrained environments. CoRR abs\/1708.08197 (2017). http:\/\/arxiv.org\/abs\/1708.08197"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73001-6_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T11:13:20Z","timestamp":1732619600000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73001-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"ISBN":["9783031730009","9783031730016"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73001-6_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]},"assertion":[{"value":"27 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}