{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T04:17:52Z","timestamp":1776399472116,"version":"3.51.2"},"reference-count":217,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>Facial recognition is one of the most academically studied and industrially developed areas within computer vision where we readily find associated applications deployed globally. This widespread adoption has uncovered significant performance variation across subjects of different racial profiles leading to focused research attention on racial bias within face recognition spanning both current causation and future potential solutions. In support, this study provides an extensive taxonomic review of research on racial bias within face recognition exploring every aspect and stage of the associated facial processing pipeline. Firstly, we discuss the problem definition of racial bias, starting with race definition, grouping strategies, and the societal implications of using race or race-related groupings. Secondly, we divide the common face recognition processing pipeline into four stages: image acquisition, face localisation, face representation, face verification and identification, and review the relevant corresponding literature associated with each stage. The overall aim is to provide comprehensive coverage of the racial bias problem with respect to each and every stage of the face recognition processing pipeline whilst also highlighting the potential pitfalls and limitations of contemporary mitigation strategies that need to be considered within future research endeavours or commercial applications alike.<\/jats:p>","DOI":"10.1145\/3705295","type":"journal-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T10:55:18Z","timestamp":1732272918000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Racial Bias within Face Recognition: A Survey"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1579-5510","authenticated-orcid":false,"given":"Seyma","family":"Yucer","sequence":"first","affiliation":[{"name":"Computer Science, Durham University, Durham, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5734-7681","authenticated-orcid":false,"given":"Furkan","family":"Tektas","sequence":"additional","affiliation":[{"name":"VisAI, Istanbul, T\u00fcrkiye"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8942-355X","authenticated-orcid":false,"given":"Noura","family":"Al Moubayed","sequence":"additional","affiliation":[{"name":"Computer Science, Durham University, Durham, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1666-7590","authenticated-orcid":false,"given":"Toby","family":"Breckon","sequence":"additional","affiliation":[{"name":"Computer Science, Durham University, Durham, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2024,12,23]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","unstructured":"Mazida A. Ahmed Ridip Dev Choudhury and Kishore Kashyap. 2022. Race estimation with deep networks. Journal of King Saud University - Computer and Information Sciences 34 7 (2022) 4579--4591. 10.1016\/j.jksuci.2020.11.029","DOI":"10.1016\/j.jksuci.2020.11.029"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.244"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-09850-1"},{"key":"e_1_3_1_5_2","unstructured":"Brandon Amos Bartosz Ludwiczuk and Mahadev Satyanarayanan. 2016. OpenFace: A general-purpose face recognition library with mobile applications. Technical Report. CMU-CS-16-118 CMU School of Computer Science."},{"key":"e_1_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Gizelle Anzures Paul C. Quinn Olivier Pascalis Alan M. Slater James W. Tanaka and Kang Lee. 2013. Developmental origins of the other-race effect. Curr Dir Psychol Sci 22 3 (June 2013) 173--178.","DOI":"10.1177\/0963721412474459"},{"key":"e_1_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Solon Barocas Anhong Guo Ece Kamar Jacquelyn Krones Meredith Ringel Morris Jennifer Wortman Vaughan Duncan Wadsworth and Hanna Wallach. 2021. Designing Disaggregated Evaluations of AI Systems: Choices Considerations and Tradeoffs. arXiv:2103.06076. Retrieved from https:\/\/arxiv.org\/abs\/2103.06076","DOI":"10.1145\/3461702.3462610"},{"key":"e_1_3_1_8_2","unstructured":"Solon Barocas Moritz Hardt and Arvind Narayanan. 2017. Fairness and Machine Learning. video. Retrieved 2024-11-01 from https:\/\/neurips.cc\/virtual\/2017\/tutorial\/8734"},{"key":"e_1_3_1_9_2","author":"Belhaouari Samir Brahim","unstructured":"Samir Brahim Belhaouari, Athmar N. M. Shamhan, and Samir Brahim Belhaouari. 2020. Fusion of deep learning and handcrafted features for intra-race recognition. Advances in Natural and Applied Sciences 14 (2020), 76--83.","journal-title":"Advances in Natural and Applied Sciences"},{"key":"e_1_3_1_10_2","volume-title":"Conf. on Fairness, Accountability, and Transparency","author":"Benthall Sebastian","year":"2019","unstructured":"Sebastian Benthall and Bruce D. Haynes. 2019. Racial categories in machine learning. In Conf. on Fairness, Accountability, and Transparency."},{"key":"e_1_3_1_11_2","volume-title":"Hyper-parameter Optimization in Deep Learning and Transfer Learning: Applications to Medical Imaging","author":"Bertrand Hadrien","year":"2019","unstructured":"Hadrien Bertrand. 2019. Hyper-parameter Optimization in Deep Learning and Transfer Learning: Applications to Medical Imaging. Ph. D. Dissertation. Universit\u00e9 Paris-Saclay."},{"key":"e_1_3_1_12_2","volume-title":"IEEE Winter Conf. on Appl. of Comput. Vis.","author":"Birhane Abeba","year":"2021","unstructured":"Abeba Birhane and Vinay Uday Prabhu. 2021. Large image datasets: A pyrrhic win for computer vision? In IEEE Winter Conf. on Appl. of Comput. Vis. IEEE."},{"key":"e_1_3_1_13_2","author":"Buolamwini Joy","unstructured":"Joy Buolamwini and Timnit Gebru. 2018. Gender Shades: Intersectional accuracy disparities in commercial gender classification. In Proc. Conf. on Fairness, Accountability and Transparency, Sorelle A. Friedler and Christo Wilson (Eds.). Vol. 81. PMLR, 77--91.","journal-title":"Proc. Conf. on Fairness, Accountability and Transparency"},{"key":"e_1_3_1_14_2","volume-title":"IEEE Int. Conf. on Auto. Face Gesture Recog.","author":"Cao Qiong","year":"2018","unstructured":"Qiong Cao, Li Shen, Weidi Xie, Omkar M. Parkhi, and Andrew Zisserman. 2018. VGGFace2: A dataset for recognising faces across pose and age. In IEEE Int. Conf. on Auto. Face Gesture Recog."},{"key":"e_1_3_1_15_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Cao Zhimin","year":"2010","unstructured":"Zhimin Cao, Qi Yin, Xiaoou Tang, and Jian Sun. 2010. Face recognition with learning-based descriptor. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_16_2","author":"Castelvecchi Davide","unstructured":"Davide Castelvecchi. 2020. Is facial recognition too biased to be let loose? Nature 587 (2020), 347--349.","journal-title":"Nature"},{"key":"e_1_3_1_17_2","author":"Chardon Alain","unstructured":"Alain Chardon, Isabelle Cretois, and Colette Hourseau. 1991. Skin colour typology and suntanning pathways. International J. Cosmetic Science 13, 4 (1991), 191--208.","journal-title":"International J. Cosmetic Science"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.fsigen.2014.08.008"},{"key":"e_1_3_1_19_2","volume-title":"Sampling Techniques: 3d Ed","author":"Cochran William Gemmell","year":"1977","unstructured":"William Gemmell Cochran. 1977. Sampling Techniques: 3d Ed. Wiley."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBIOM.2019.2897801"},{"key":"e_1_3_1_21_2","volume-title":"Int. Conf. on Machine Learning","author":"Creager Elliot","year":"2019","unstructured":"Elliot Creager, David Madras, J\u00f6rn-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel. 2019. Flexibly fair representation learning by disentanglement. In Int. Conf. on Machine Learning. PMLR."},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2018.09.002"},{"key":"e_1_3_1_23_2","author":"Daly Angela","unstructured":"Angela Daly. 2019. Global information society watch 2019 Report. Algorithmic oppression with Chinese characteristics: AI against Xinjiang's Uyghurs. APC, 108--112.","journal-title":"Global information society watch 2019 Report"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2015.2480381"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1353\/hub.2007.0045"},{"key":"e_1_3_1_26_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Deng Jiankang","year":"2020","unstructured":"Jiankang Deng, Jia Guo, Evangelos Ververas, Irene Kotsia, and Stefanos Zafeiriou. 2020. RetinaFace: Single-shot multi-level face localisation in the wild. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_27_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Deng Jiankang","year":"2019","unstructured":"Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. 2019. Arcface: Additive angular margin loss for deep face recognition. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_28_2","volume-title":"Int. Conf. Comput. Vis.","author":"Dhar Prithviraj","year":"2021","unstructured":"Prithviraj Dhar, Joshua Gleason, Aniket Roy, Carlos D. Castillo, and Rama Chellappa. 2021. PASS: Protected attribute suppression system for mitigating bias in face recognition. In Int. Conf. Comput. Vis."},{"key":"e_1_3_1_29_2","unstructured":"Prithviraj Dhar Joshua Gleason Aniket Roy Carlos D. Castillo P. Jonathon Phillips and Rama Chellappa. 2022. Distill and De-bias: Mitigating Bias in Face Verification using Knowledge Distillation. https:\/\/arxiv.org\/abs\/2112.09786"},{"key":"e_1_3_1_30_2","unstructured":"Prithviraj Dhar Joshua Gleason Hossein Souri Carlos D. Castillo and Rama Chellappa. 2020. Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face Recognition. 2006.07845 [cs.CV] https:\/\/arxiv.org\/abs\/2006.07845"},{"key":"e_1_3_1_31_2","unstructured":"Samuel Dooley Tom Goldstein and John P. Dickerson. 2021. Robustness disparities in commercial face detection. arXiv:2108.12508. Retrieved from https:\/\/arxiv.org\/abs\/2108.12508"},{"key":"e_1_3_1_32_2","unstructured":"Samuel Dooley George Z. Wei Tom Goldstein and John P. Dickerson. 2022. Are commercial face detection models as biased as academic models? arXiv:2201.10047. Retrieved from https:\/\/arxiv.org\/abs\/2201.10047"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TTS.2020.2992344"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3507902"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"e_1_3_1_36_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Faraki Masoud","year":"2021","unstructured":"Masoud Faraki, Xiang Yu, Yi-Hsuan Tsai, Yumin Suh, and Manmohan Chandraker. 2021. Cross-domain similarity learning for face recognition in unseen domains. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_37_2","doi-asserted-by":"crossref","unstructured":"Leslie G. Farkas Marko J. Katic Christopher R. Forrest Kurt W. Alt Ivana Bagic Georgi Baltadjiev Eugenia Cunha Marta Cvicelov\u00e1 Scott Davies Ilse Erasmus Rhonda Gillett-Netting Karel Hajnis Arianne Kemkes-Grottenthaler Irena Khomyakova Ashizava Kumi J. Stranger Kgamphe Nakamura Kayo-daigo Thuy Le Andrzej Malinowski Marina Negasheva Sotiris Manolis Murat Oget\u00fcrk Ramin Parvizrad Friedrich R\u00f6sing Paresh Sahu Chiarella Sforza Stefan Sivkov Nigar Sultanova Tatjana Tomazo-Ravnik G\u00e1bor T\u00f3th Ahmet Uzun and Eman Yahia. 2005. International anthropometric study of facial morphology in various ethnic groups\/races. J. Craniofac Surg. 16 4 (July 2005) 615--646.","DOI":"10.1097\/01.scs.0000171847.58031.9e"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1177\/0002764215613401"},{"key":"e_1_3_1_39_2","author":"Fitzpatrick Thomas B.","unstructured":"Thomas B. Fitzpatrick. 1975. Soleil et peau. J. Med Esthet 2 (1975), 33--34.","journal-title":"J. Med Esthet"},{"key":"e_1_3_1_40_2","author":"Fitzpatrick T. B.","unstructured":"T. B. Fitzpatrick. 1988. The validity and practicality of sun-reactive skin types I through VI. Arch Dermatol 124, 6 (June 1988), 869--871.","journal-title":"Arch Dermatol"},{"key":"e_1_3_1_41_2","volume-title":"Proc. of the 2021 AAAI\/ACM Conference on AI, Ethics, and Society","author":"Fleisher Will","year":"2021","unstructured":"Will Fleisher. 2021. What\u2019s fair about individual fairness? In Proc. of the 2021 AAAI\/ACM Conference on AI, Ethics, and Society. ACM."},{"key":"e_1_3_1_42_2","unstructured":"ISO Central Secretary. 2011. Information technology - Biometric data interchange formats - Part 5: Face image data. Standard ISO\/IEC 19794-5:2011. International Organization for Standardization Geneva CH. https:\/\/www.iso.org\/standard\/50867.html"},{"key":"e_1_3_1_43_2","volume-title":"IEEE Int. Conf. on Biometrics","author":"Garcia Raul Vicente","year":"2019","unstructured":"Raul Vicente Garcia, Lukasz Wandzik, Louisa Grabner, and Joerg Krueger. 2019. The harms of demographic bias in deep face recognition research. In IEEE Int. Conf. on Biometrics."},{"key":"e_1_3_1_44_2","volume-title":"IEEE Int. Joint Conf. on Biometrics","author":"Ge Jiancheng","year":"2020","unstructured":"Jiancheng Ge, Weihong Deng, Mei Wang, and Jiani Hu. 2020. FGAN: Fan-shaped GAN for racial transformation. In IEEE Int. Joint Conf. on Biometrics."},{"key":"e_1_3_1_45_2","doi-asserted-by":"crossref","unstructured":"Timnit Gebru Jamie Morgenstern Briana Vecchione Jennifer Wortman Vaughan Hanna Wallach Hal Daum\u00e9 Iii and Kate Crawford. 2021. Datasheets for datasets. Commun. of the ACM 64 12 (2021) 86--92.","DOI":"10.1145\/3458723"},{"key":"e_1_3_1_46_2","volume-title":"Eur. Conf. Comput. Vis.","author":"Gong Sixue","year":"2020","unstructured":"Sixue Gong, Xiaoming Liu, and Anil K. Jain. 2020. Jointly De-biasing face recognition and demographic attribute estimation. In Eur. Conf. Comput. Vis.Springer-Verlag."},{"key":"e_1_3_1_47_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Gong Sixue","year":"2021","unstructured":"Sixue Gong, Xiaoming Liu, and Anil K. Jain. 2021. Mitigating face recognition bias via group adaptive classifier. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1080\/01419870.2018.1444781"},{"key":"e_1_3_1_49_2","author":"Gravett Willem H.","unstructured":"Willem H. Gravett. 2020. Digital coloniser? China and artificial intelligence in Africa. Survival 62, 6 (2020), 153--178.","journal-title":"Survival"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-020-01123-z"},{"key":"e_1_3_1_51_2","doi-asserted-by":"crossref","DOI":"10.6028\/NIST.IR.8271","volume-title":"Face Recognition Vendor Test (FRVT) Part 2: Identification","author":"Grother Patrick","year":"2019","unstructured":"Patrick Grother, Patrick Grother, Mei Ngan, and Kayee Hanaoka. 2019. Face Recognition Vendor Test (FRVT) Part 2: Identification. Technical Report. National Institute of Standards and Technology."},{"key":"e_1_3_1_52_2","author":"Grother Patrick","unstructured":"Patrick Grother and Mei Ngan. 2017. The IJB-A face identification challenge performance report. Technical Report. National Institute of Standards and Technology.","journal-title":"Technical Report. National Institute of Standards and Technology"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhevol.2014.08.001"},{"key":"e_1_3_1_54_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Guo Jianzhu","year":"2020","unstructured":"Jianzhu Guo, Xiangyu Zhu, Chenxu Zhao, Dong Cao, Zhen Lei, and Stan Z. Li. 2020. Learning meta face recognition in unseen domains. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_55_2","volume-title":"Eur. Conf. Comput. Vis.","author":"Guo Yandong","year":"2016","unstructured":"Yandong Guo, Lei Zhang, Yuxiao Hu, Xiaodong He, and Jianfeng Gao. 2016. MS-Celeb-1M: A dataset and benchmark for large-scale face recognition. In Eur. Conf. Comput. Vis. Springer."},{"key":"e_1_3_1_56_2","doi-asserted-by":"crossref","DOI":"10.1145\/3351095.3372826","article-title":"Towards a critical race methodology in algorithmic fairness.","author":"Hanna Alex","year":"2020","unstructured":"Alex Hanna, Emily Denton, Andrew Smart, and Jamila Smith-Loud. 2020. Towards a critical race methodology in algorithmic fairness. FAT* 2020 - Proc. of the 2020 Conf. on Fairness, Accountability, and Transparency.","journal-title":"FAT* 2020 - Proc. of the 2020 Conf. on Fairness, Accountability, and Transparency"},{"key":"e_1_3_1_57_2","author":"Hardt Moritz","unstructured":"Moritz Hardt, Eric Price, and Nathan Srebro. 2016. Equality of opportunity in supervised learning. In Proceedings of the 30th International Conference on Neural Information Processing Systems (Barcelona, Spain) (NIPS'16). Curran Associates Inc., Red Hook, NY, USA, 3323--3331.","journal-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems (Barcelona, Spain) (NIPS'16)"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1093\/sf\/72.2.451"},{"key":"e_1_3_1_59_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Hazirbas Caner","year":"2021","unstructured":"Caner Hazirbas, Joanna Bitton, Brian Dolhansky, Jacqueline Pan, Albert Gordo, and Cristian Canton Ferrer. 2021. Casual conversations: A dataset for measuring fairness in AI. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_60_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"He Kaiming","year":"2016","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_61_2","unstructured":"Lanqing He Zhongdao Wang Yali Li and Shengjin Wang. 2019. Softmax dissection: Towards understanding intra- and inter-class objective for embedding learning. arXiv:1908.01281. Retrieved from https:\/\/arxiv.org\/abs\/1908.01281"},{"key":"e_1_3_1_62_2","unstructured":"Yonghao He Dezhong Xu Lifang Wu Meng Jian Shiming Xiang and Chunhong Pan. 2019. LFFD: A light and fast face detector for edge devices. arXiv:1904.10633. Retrieved from https:\/\/arxiv.org\/abs\/1904.10633"},{"key":"e_1_3_1_63_2","unstructured":"Thomas Hellstr\u00f6m Virginia Dignum and Suna Bensch. 2020. Bias in machine learning\u2013what is it good for? arXiv:2004.00686. Retrieved from https:\/\/arxiv.org\/abs\/2004.00686"},{"key":"e_1_3_1_64_2","author":"Hepple Bob","unstructured":"Bob Hepple. 2010. The new single equality act in Britain. The Equal Rights Review 5 (2010), 11--24.","journal-title":"The Equal Rights Review"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1080\/17470210600654750"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1057\/s41292-020-00190-9"},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBIOM.2021.3123550"},{"key":"e_1_3_1_68_2","volume-title":"Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments","author":"Huang Gary B.","year":"2007","unstructured":"Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. 2007. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. Technical Report. University of Massachusetts, Amherst."},{"key":"e_1_3_1_69_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Huang Yuge","year":"2020","unstructured":"Yuge Huang, Yuhan Wang, Ying Tai, Xiaoming Liu, Pengcheng Shen, Shaoxin Li, Jilin Li, and Feiyue Huang. 2020. Curricularface: Adaptive curriculum learning loss for deep face recognition. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_70_2","volume-title":"IEEE Int. Conf. on Auto. Face and Gesture Recog.","author":"Hupont Isabelle","year":"2019","unstructured":"Isabelle Hupont and Carles Fern\u00e1ndez. 2019. DemogPairs: Quantifying the impact of demographic imbalance in deep face recognition. In IEEE Int. Conf. on Auto. Face and Gesture Recog."},{"key":"e_1_3_1_71_2","volume-title":"Facial Recognition Technology A Survey of Policy and Implementation Issues","author":"Introna Lucas D.","year":"2010","unstructured":"Lucas D. Introna and Helen Nissenbaum. 2010. Facial Recognition Technology A Survey of Policy and Implementation Issues. Technical Report."},{"key":"e_1_3_1_72_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Jung S.","year":"2021","unstructured":"S. Jung, D. Lee, T. Park, and T. Moon. 2021. Fair feature distillation for visual recognition. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_73_2","doi-asserted-by":"publisher","DOI":"10.1177\/1368430210374609"},{"key":"e_1_3_1_74_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.06.010"},{"key":"e_1_3_1_75_2","volume-title":"Mach. Learn. Conf. Belgium and The Netherlands","author":"Kamiran Faisal","year":"2010","unstructured":"Faisal Kamiran and Toon Calders. 2010. Classification with no discrimination by preferential sampling. In Mach. Learn. Conf. Belgium and The Netherlands."},{"key":"e_1_3_1_76_2","volume-title":"Int. Conf. of the Biometrics Special Interest Group (BIOSIG)","author":"Karahan Samil","year":"2016","unstructured":"Samil Karahan, Merve Kilinc Yildirum, Kadir Kirtac, Ferhat Sukru Rende, Gultekin Butun, and Hazim Kemal Ekenel. 2016. How image degradations affect deep CNN-based face recognition?. In Int. Conf. of the Biometrics Special Interest Group (BIOSIG). IEEE."},{"key":"e_1_3_1_77_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Karras Tero","year":"2020","unstructured":"Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2020. Analyzing and improving the image quality of stylegan. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_78_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Kemelmacher-Shlizerman Ira","year":"2016","unstructured":"Ira Kemelmacher-Shlizerman, Steven M. Seitz, Daniel Miller, and Evan Brossard. 2016. The megaface benchmark: 1 million faces for recognition at scale. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_79_2","volume-title":"Proc. ACM Conf. on Fairness, Accountability, and Transparency","author":"Khan Zaid","year":"2021","unstructured":"Zaid Khan and Yun Fu. 2021. One label, one billion faces: Usage and consistency of racial categories in computer vision. In Proc. ACM Conf. on Fairness, Accountability, and Transparency."},{"key":"e_1_3_1_80_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Kim B.","year":"2019","unstructured":"B. Kim, H. Kim, K. Kim, S. Kim, and J. Kim. 2019. Learning not to learn: Training DNNs with biased data. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_81_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1083-6101.2007.00392.x"},{"key":"e_1_3_1_82_2","doi-asserted-by":"publisher","DOI":"10.5555\/1577069.1755843"},{"key":"e_1_3_1_83_2","unstructured":"Newton M. Kinyanjui Timothy Odonga Celia Cintas Noel C. F. Codella Rameswar Panda Prasanna Sattigeri and Kush R. Varshney. 2019. Estimating skin tone and effects on classification performance in dermatology datasets. arXiv:1910.13268. Retrieved from https:\/\/arxiv.org\/abs\/1910.13268"},{"key":"e_1_3_1_84_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2012.2214212"},{"key":"e_1_3_1_85_2","unstructured":"Martin Knoche Mohamed Elkadeem Stefan H\u00f6rmann and Gerhard Rigoll. 2022. Octuplet Loss: Make face recognition robust to image resolution. arXiv:2207.06726. Retrieved from https:\/\/arxiv.org\/abs\/2207.06726"},{"key":"e_1_3_1_86_2","volume-title":"Proc. of the Int. Conf. on Commun. and Sign. Process.","author":"Kolkur S.","year":"2016","unstructured":"S. Kolkur, D. Kalbande, P. Shimpi, C. Bapat, and J. Jatakia. 2016. Human skin detection using RGB, HSV and YCbCr color models. In Proc. of the Int. Conf. on Commun. and Sign. Process."},{"key":"e_1_3_1_87_2","volume-title":"Automated Fingerprint Identification Systems (AFIS)","author":"Komarinski Peter","year":"2005","unstructured":"Peter Komarinski. 2005. Automated Fingerprint Identification Systems (AFIS). Elsevier."},{"key":"e_1_3_1_88_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20020342"},{"key":"e_1_3_1_89_2","doi-asserted-by":"publisher","DOI":"10.1109\/TTS.2020.2974996"},{"key":"e_1_3_1_90_2","unstructured":"K. S. Krishnapriya Michael C. King and Kevin W. Bowyer. 2021. Analysis of manual and automated skin tone assignments for face recognition applications. arXiv:2104.14685. Retrieved from https:\/\/arxiv.org\/abs\/2104.14685"},{"key":"e_1_3_1_91_2","unstructured":"Matt Kusner Joshua Loftus Chris Russell and Ricardo Silva. 2017. Counterfactual fairness. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach California USA) (NIPS'17). Curran Associates Inc. Red Hook NY USA 4069--4079."},{"key":"e_1_3_1_92_2","volume-title":"IEEE Winter Conf. on Appl. of Comput. Vis.","author":"K\u00e4rkk\u00e4inen Kimmo","year":"2021","unstructured":"Kimmo K\u00e4rkk\u00e4inen and Jungseock Joo. 2021. FairFace: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. In IEEE Winter Conf. on Appl. of Comput. Vis."},{"key":"e_1_3_1_93_2","doi-asserted-by":"publisher","DOI":"10.1145\/2933241"},{"key":"e_1_3_1_94_2","doi-asserted-by":"crossref","unstructured":"Y. Lee E. Lee and W. J. Park. 2000. Anchor epicanthoplasty combined with out-fold type double eyelidplasty for Asians: Do we have to make an additional scar to correct the Asian epicanthal fold? Plast Reconstr Surg 105 5 (April 2000) 1872--1880.","DOI":"10.1097\/00006534-200004050-00040"},{"key":"e_1_3_1_95_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.112"},{"key":"e_1_3_1_96_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Li Jian","year":"2019","unstructured":"Jian Li, Yabiao Wang, Changan Wang, Ying Tai, Jianjun Qian, Jian Yang, Chengjie Wang, Jilin Li, and Feiyue Huang. 2019. DSFD: Dual shot face detector. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_97_2","doi-asserted-by":"crossref","unstructured":"Rasmus Rothe Radu Timofte and Luc Van Gool. 2018. Deep expectation of real and apparent age from a single image without facial landmarks. International Journal of Computer Vision 126 2--4 (2018). https:\/\/data.vision.ee.ethz.ch\/cvl\/rrothe\/imdb-wiki\/","DOI":"10.1007\/s11263-016-0940-3"},{"key":"e_1_3_1_98_2","volume-title":"Systema Naturae","author":"Linnaeus Carolus","year":"1758","unstructured":"Carolus Linnaeus. 1758. Systema Naturae. Stockholm Holmiae (Laurentii Salvii)."},{"key":"e_1_3_1_99_2","volume-title":"Int. Conf. Comput. Vis. Worksh.","author":"Liu Boxiao","year":"2021","unstructured":"Boxiao Liu, Shenghan Zhang, Guanglu Song, Haihang You, and Yu Liu. 2021. Rectifying the data bias in knowledge distillation. In Int. Conf. Comput. Vis. Worksh."},{"key":"e_1_3_1_100_2","doi-asserted-by":"crossref","unstructured":"Chengjun Liu and Harry Wechsler. 2002. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. on Image Process. 11 4 (2002) 467--476.","DOI":"10.1109\/TIP.2002.999679"},{"key":"e_1_3_1_101_2","volume-title":"Eur. Conf. Comput. Vis.","author":"Liu Jiaheng","year":"2022","unstructured":"Jiaheng Liu, Zhipeng Yu, Haoyu Qin, Yichao Wu, Ding Liang, Gangming Zhao, and Ke Xu. 2022. OneFace: One threshold for all. In Eur. Conf. Comput. Vis. Springer."},{"key":"e_1_3_1_102_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Liu Weiyang","year":"2017","unstructured":"Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, and Le Song. 2017. Sphereface: Deep hypersphere embedding for face recognition. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_103_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1077-3142(03)00078-X"},{"key":"e_1_3_1_104_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Liu Yang","year":"2022","unstructured":"Yang Liu, Fei Wang, Jiankang Deng, Zhipeng Zhou, Baigui Sun, and Hao Li. 2022. MogFace: Towards a deeper appreciation on face detection. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_105_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jid.2019.11.003"},{"key":"e_1_3_1_106_2","doi-asserted-by":"publisher","DOI":"10.1207\/s15327957pspr0804_4"},{"key":"e_1_3_1_107_2","doi-asserted-by":"publisher","DOI":"10.1177\/2372732217747086"},{"key":"e_1_3_1_108_2","volume-title":"Proc. IEEE\/CVF Int. Conf. on Comput. Vis.","author":"Majumdar Puspita","year":"2021","unstructured":"Puspita Majumdar, Surbhi Mittal, Richa Singh, and Mayank Vatsa. 2021. Unravelling the effect of image distortions for biased prediction of pre-trained face recognition models. In Proc. IEEE\/CVF Int. Conf. on Comput. Vis."},{"key":"e_1_3_1_109_2","volume-title":"Int. Conf. on Biometrics (ICB)","author":"Maze Brianna","year":"2018","unstructured":"Brianna Maze, Jocelyn Adams, James A. Duncan, Nathan Kalka, Tim Miller, Charles Otto, Anil K. Jain, W. Tyler Niggel, Janet Anderson, Jordan Cheney, and Patrick Grother. 2018. IARPA janus benchmark - C: face dataset and protocol. In Int. Conf. on Biometrics (ICB). IEEE."},{"key":"e_1_3_1_110_2","doi-asserted-by":"publisher","DOI":"10.1111\/aman.13385"},{"key":"e_1_3_1_111_2","doi-asserted-by":"publisher","DOI":"10.1145\/3457607"},{"key":"e_1_3_1_112_2","doi-asserted-by":"publisher","DOI":"10.1037\/1076-8971.7.1.3"},{"key":"e_1_3_1_113_2","volume-title":"IEEE Conf. on Graphics, Patterns and Images","author":"Menezes Hanna F.","year":"2021","unstructured":"Hanna F. Menezes, Arthur S. C. Ferreira, Eanes T. Pereira, and Herman M. Gomes. 2021. Bias and fairness in face detection. In IEEE Conf. on Graphics, Patterns and Images."},{"key":"e_1_3_1_114_2","article-title":"Microsoft Turned Down Facial-recognition Sales on Human Rights Concerns","author":"Menn Joseph","year":"2019","unstructured":"Joseph Menn. 2019. Microsoft Turned Down Facial-recognition Sales on Human Rights Concerns. Reuters.","journal-title":"Reuters"},{"key":"e_1_3_1_115_2","unstructured":"Michele Merler Nalini Ratha Rogerio S. Feris and John R. Smith. 2019. Diversity in Faces. 1901.10436 [cs.CV] https:\/\/arxiv.org\/abs\/1901.10436"},{"key":"e_1_3_1_116_2","doi-asserted-by":"publisher","DOI":"10.1145\/3415186"},{"key":"e_1_3_1_117_2","unstructured":"Shervin Minaee Ping Luo Zhe Lin and Kevin Bowyer. 2021. Going Deeper Into Face Detection: A Survey. 2103.14983 [cs.CV] https:\/\/arxiv.org\/abs\/2103.14983"},{"key":"e_1_3_1_118_2","volume-title":"Proc. of the AAAI\/ACM Conf. on AI, Ethics, and Society","author":"Mitchell Margaret","year":"2020","unstructured":"Margaret Mitchell, Dylan Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, and Jamie Morgenstern. 2020. Diversity and inclusion metrics in subset selection. In Proc. of the AAAI\/ACM Conf. on AI, Ethics, and Society. ACM."},{"key":"e_1_3_1_119_2","volume-title":"The Need for Biases in Learning Generalizations","author":"Mitchell Tom M.","year":"1980","unstructured":"Tom M. Mitchell. 1980. The Need for Biases in Learning Generalizations. Dep. of Comput. Science, Laboratory for Comput. Science Research."},{"key":"e_1_3_1_120_2","volume-title":"IEEE Int. Conf. on Auto. Face and Gesture Recog.","author":"Mittal Surbhi","year":"2023","unstructured":"Surbhi Mittal, Kartik Thakral, Puspita Majumdar, Mayank Vatsa, and Richa Singh. 2023. Are face detection models biased?. In IEEE Int. Conf. on Auto. Face and Gesture Recog."},{"key":"e_1_3_1_121_2","doi-asserted-by":"publisher","unstructured":"Ellis Monk. 2023. The Monk Skin Tone Scale. 10.31235\/osf.io\/pdf4c","DOI":"10.31235\/osf.io\/pdf4c"},{"key":"e_1_3_1_122_2","volume-title":"Machine-readable Travel Documents","author":"Monnerat Jean","year":"2007","unstructured":"Jean Monnerat, Serge Vaudenay, and Martin Vuagnoux. 2007. Machine-readable Travel Documents. Technical Report. Springer."},{"key":"e_1_3_1_123_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3015420"},{"key":"e_1_3_1_124_2","unstructured":"Paul Mozur. 2019. One Month 500 000 Face Scans: How China Is Using A.I. to Profile a Minority. The New York Times (2019). Retrieved 2024-11-01 from https:\/\/www.nytimes.com\/2019\/04\/14\/technology\/china-surveillance-artificial-intelligence-racial-profiling.html"},{"key":"e_1_3_1_125_2","volume-title":"International Conference on Learning Representations","unstructured":"Ching-Yao Chuang and Youssef Mroueh. 2021. Fair Mixup: Fairness via interpolation. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=DNl5s5BXeBn"},{"key":"e_1_3_1_126_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3080496"},{"key":"e_1_3_1_127_2","doi-asserted-by":"crossref","DOI":"10.4159\/9780674240919","volume-title":"The Condemnation of Blackness","author":"Muhammad Khalil Gibran","year":"2019","unstructured":"Khalil Gibran Muhammad. 2019. The Condemnation of Blackness. Harvard University Press."},{"key":"e_1_3_1_128_2","author":"M\u00fcller-Wille Staffan","unstructured":"Staffan M\u00fcller-Wille. 2014. Race and history: Comments from an epistemological point of view. Sci Technol Human Values 39, 4 (July 2014), 597--606.","journal-title":"Sci Technol Human Values"},{"key":"e_1_3_1_129_2","volume-title":"Measurements of Skin Colour","author":"Munidasa Deepani","year":"2018","unstructured":"Deepani Munidasa, Gerrit Schlippe, and Sharnika Abeyakirthi. 2018. Measurements of Skin Colour. Springer Int. Publishing, Cham."},{"key":"e_1_3_1_130_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00282"},{"key":"e_1_3_1_131_2","unstructured":"Vidya Muthukumar Tejaswini Pedapati Nalini Ratha Prasanna Sattigeri Chai-Wah Wu Brian Kingsbury Abhishek Kumar Samuel Thomas Aleksandra Mojsilovic and Kush R. Varshney. 2018. Understanding unequal gender classification accuracy from face images. arXiv:1812.00099. Retrieved from https:\/\/arxiv.org\/abs\/1812.00099"},{"key":"e_1_3_1_132_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Nam H.","year":"2021","unstructured":"H. Nam, H. Lee, J. Park, W. Yoon, and D. Yoo. 2021. Reducing domain gap by reducing style bias. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_133_2","doi-asserted-by":"publisher","unstructured":"Pamela E. Oliver. 2017. Race Names. 10.31235\/osf.io\/7wys2","DOI":"10.31235\/osf.io\/7wys2"},{"key":"e_1_3_1_134_2","volume-title":"Racial Formation in the United States","author":"Omi Michael","year":"2014","unstructured":"Michael Omi and Howard Winant. 2014. Racial Formation in the United States. Routledge."},{"key":"e_1_3_1_135_2","doi-asserted-by":"publisher","DOI":"10.1145\/3527152"},{"key":"e_1_3_1_136_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2011.12.007"},{"key":"e_1_3_1_137_2","doi-asserted-by":"publisher","DOI":"10.1002\/ajpa.21006"},{"key":"e_1_3_1_138_2","doi-asserted-by":"publisher","unstructured":"Omkar M. Parkhi Andrea Vedaldi and Andrew Zisserman. 2015. Deep Face Recognition. Article 41 (September 2015) 12 pages. 10.5244\/C.29.41","DOI":"10.5244\/C.29.41"},{"key":"e_1_3_1_139_2","doi-asserted-by":"publisher","DOI":"10.1145\/1870076.1870082"},{"key":"e_1_3_1_140_2","doi-asserted-by":"publisher","DOI":"10.1109\/34.879790"},{"key":"e_1_3_1_141_2","unstructured":"Latrice C. Pichon Hope Landrine Irma Corral Yongping Hao Joni A. Mayer and Katherine D. Hoerster. 2010. Measuring skin cancer risk in African Americans: Is the Fitzpatrick Skin Type Classification Scale culturally sensitive? Ethn Dis 20 2 (2010) 174--179."},{"key":"e_1_3_1_142_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2017.01208"},{"key":"e_1_3_1_143_2","volume-title":"IEEE Int. Conf. on Pattern Recog.","author":"Poyser Matt","year":"2021","unstructured":"Matt Poyser, Amir Atapour-Abarghouei, and Toby P. Breckon. 2021. On the impact of lossy image and video compression on the performance of DCNNs. In IEEE Int. Conf. on Pattern Recog."},{"key":"e_1_3_1_144_2","doi-asserted-by":"crossref","unstructured":"Delong Qi Weijun Tan Qi Yao and Jingfeng Liu. 2023. YOLO5Face: Why reinventing a face detector. In Computer Vision - ECCV 2022 Workshops. Springer Nature Switzerland Cham 228--244.","DOI":"10.1007\/978-3-031-25072-9_15"},{"key":"e_1_3_1_145_2","unstructured":"Aoyu Qin. 2020. Asymmetric rejection loss for fairer face recognition. 2002.03276 [cs.CV] https:\/\/arxiv.org\/abs\/2002.03276"},{"key":"e_1_3_1_146_2","volume-title":"Int. Congress of Philosophy","author":"Quine Willard V.","year":"1953","unstructured":"Willard V. Quine. 1953. Three grades of modal involvment. In Int. Congress of Philosophy."},{"key":"e_1_3_1_147_2","doi-asserted-by":"crossref","unstructured":"Ruggero Ragonesi Riccardo Volpi Jacopo Cavazza and Vittorio Murino. 2021. Learning unbiased representations via mutual information backpropagation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. 2729--2738.","DOI":"10.1109\/CVPRW53098.2021.00307"},{"key":"e_1_3_1_148_2","volume-title":"Proc. of the 2019 AAAI\/ACM Conf. on AI, Ethics, and Society","author":"Raji Inioluwa Deborah","year":"2019","unstructured":"Inioluwa Deborah Raji and Joy Buolamwini. 2019. Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products. In Proc. of the 2019 AAAI\/ACM Conf. on AI, Ethics, and Society."},{"key":"e_1_3_1_149_2","volume-title":"AAAI\/ACM Conf. on AI, Ethics, and Society","author":"Raji Inioluwa Deborah","year":"2020","unstructured":"Inioluwa Deborah Raji, Timnit Gebru, Margaret Mitchell, Joy Buolamwini, Joonseok Lee, and Emily Denton. 2020. Saving face: Investigating the ethical concerns of facial recognition auditing. In AAAI\/ACM Conf. on AI, Ethics, and Society."},{"key":"e_1_3_1_150_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Ramanathan Narayanan","year":"2006","unstructured":"Narayanan Ramanathan and Rama Chellappa. 2006. Modeling age progression in young faces. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_151_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.genet.37.110801.143233"},{"key":"e_1_3_1_152_2","doi-asserted-by":"publisher","DOI":"10.1068\/p6110"},{"key":"e_1_3_1_153_2","volume-title":"IEEE Int. Conf. on Auto. Face and Gesture Recog.","author":"Ricanek Karl","year":"2006","unstructured":"Karl Ricanek and Tamirat Tesafaye. 2006. MORPH: A longitudinal image database of normal adult age-progression. In IEEE Int. Conf. on Auto. Face and Gesture Recog."},{"key":"e_1_3_1_154_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3282837"},{"key":"e_1_3_1_155_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog. Worksh.","author":"Robinson Joseph P.","year":"2020","unstructured":"Joseph P. Robinson, Gennady Livitz, Yann Henon, Can Qin, Yun Fu, and Samson Timoner. 2020. Face recognition: Too bias, or not too bias?. In IEEE Conf. Comput. Vis. Pattern Recog. Worksh."},{"key":"e_1_3_1_156_2","unstructured":"Yuji Roh Kangwook Lee Steven Whang and Changho Suh. 2021. Sample selection for fair and robust training. In Advances in Neural Information Processing Systems M. Ranzato A. Beygelzimer Y. Dauphin P. S. Liang and J. Wortman Vaughan (Eds.). Vol. 34. Curran Associates Inc. 815--827. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2021\/file\/07563a3fe3bbe7e3ba84431ad9d055af-Paper.pdf"},{"key":"e_1_3_1_157_2","volume-title":"IEEE Int. Conf. on Comput. Vis. Worksh.","author":"Rothe Rasmus","year":"2015","unstructured":"Rasmus Rothe, Radu Timofte, and Luc Van Gool. 2015. DEX: Deep expectation of apparent age from a single image. In IEEE Int. Conf. on Comput. Vis. Worksh."},{"key":"e_1_3_1_158_2","volume-title":"Genetic Maps and Human Imaginations: The Limits of Science in Understanding Who We Are","author":"Rothman Barbara Katz","year":"1998","unstructured":"Barbara Katz Rothman. 1998. Genetic Maps and Human Imaginations: The Limits of Science in Understanding Who We Are. WW Norton and Company."},{"key":"e_1_3_1_159_2","unstructured":"Hee Jung Ryu Hartwig Adam and Margaret Mitchell. 2018. InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity. 1712.00193 [cs.CV] https:\/\/arxiv.org\/abs\/1712.00193"},{"key":"e_1_3_1_160_2","volume-title":"Int. Joint Conf. on Neural Networks","author":"Sadhukhan Payel","year":"2019","unstructured":"Payel Sadhukhan. 2019. Learning minority class prior to minority oversampling. In Int. Joint Conf. on Neural Networks. IEEE."},{"key":"e_1_3_1_161_2","volume-title":"Int. Conf. Learn. Represent.","author":"Salvador Tiago","year":"2022","unstructured":"Tiago Salvador, Stephanie Cairns, Vikram Voleti, Noah Marshall, and Adam M. Oberman. 2022. FairCal: Fairness calibration for face verification. In Int. Conf. Learn. Represent."},{"key":"e_1_3_1_162_2","author":"Schendel Stephen A.","unstructured":"Stephen A. Schendel. 1995. Anthropometry of the head and face. Plastic and Reconstructive Surgery 96, 2 (1995), 480.","journal-title":"Plastic and Reconstructive Surgery"},{"key":"e_1_3_1_163_2","unstructured":"Peter M. Schneider Barbara Prainsack and Manfred Kayser. 2019. The use of forensic DNA phenotyping in predicting appearance and biogeographic ancestry. Dtsch Arztebl Int 51-52 51-52 (Dec. 2019) 873--880."},{"key":"e_1_3_1_164_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Schroff Florian","year":"2015","unstructured":"Florian Schroff, Dmitry Kalenichenko, and James Philbin. 2015. Facenet: A unified embedding for face recognition and clustering. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_165_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2022.103682"},{"key":"e_1_3_1_166_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10539-009-9193-7"},{"key":"e_1_3_1_167_2","unstructured":"Stavros Shiaeles. 2021. Facebook will drop its facial recognition system - but heres why we should be sceptical. The Conversation (2021). Retrieved 2024-11-01 from https:\/\/theconversation.com\/facebook-will-drop-its-facial-recognition-system-but-heres-why-we-should-be-sceptical-171186"},{"key":"e_1_3_1_168_2","author":"Simonyan Karen","unstructured":"Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations.","journal-title":"International Conference on Learning Representations"},{"key":"e_1_3_1_169_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-65414-6_32"},{"key":"e_1_3_1_170_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jesp.2014.11.011"},{"key":"e_1_3_1_171_2","doi-asserted-by":"publisher","DOI":"10.1109\/79.952804"},{"key":"e_1_3_1_172_2","unstructured":"Olivia Solon. 2019. Facial recognition's \u2018dirty little secret\u2019: Social media photos used without consent. Retrieved 2024-11-01 from https:\/\/www.nbcnews.com\/tech\/internet\/n981921"},{"key":"e_1_3_1_173_2","doi-asserted-by":"crossref","unstructured":"Marilyn S. Sommers Jamison D. Fargo Yadira Regueira Kathleen M. Brown Barbara L. Beacham Angela R. Perfetti Janine S. Everett and David J. Margolis. 2019. Are the fitzpatrick skin phototypes valid for cancer risk assessment in a racially and ethnically diverse sample of women? Ethn Dis 29 3 (July 2019) 505--512.","DOI":"10.18865\/ed.29.3.505"},{"key":"e_1_3_1_174_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog. Worksh.","author":"Srinivas Nisha","year":"2019","unstructured":"Nisha Srinivas, Karl Ricanek, Dana Michalski, David S. Bolme, and Michael King. 2019. Face recognition algorithm bias: Performance differences on images of children and adults. In IEEE Conf. Comput. Vis. Pattern Recog. Worksh."},{"key":"e_1_3_1_175_2","volume-title":"Pattern Recognition","author":"Sun Yufei","year":"2021","unstructured":"Yufei Sun, Yong Li, and Zhen Cui. 2021. NFW: Towards national and individual fairness in face recognition. In Pattern Recognition."},{"key":"e_1_3_1_176_2","doi-asserted-by":"publisher","DOI":"10.1145\/3465416.3483305"},{"key":"e_1_3_1_177_2","unstructured":"Shuhan Tan Yujun Shen and Bolei Zhou. 2021. Improving the Fairness of Deep Generative Models without Retraining. (2021). 2012.04842 [cs.CV] https:\/\/arxiv.org\/abs\/2012.04842"},{"key":"e_1_3_1_178_2","volume-title":"Eur. Conf. Comput. Vis.","author":"Tang Xu","year":"2018","unstructured":"Xu Tang, Daniel Du, Zeqiang He, and Jingtuo Liu. 2018. PyramidBox: A context-assisted single shot face detector. In Eur. Conf. Comput. Vis."},{"key":"e_1_3_1_179_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.11.007"},{"key":"e_1_3_1_180_2","doi-asserted-by":"publisher","DOI":"10.1109\/TTS.2021.3111823"},{"key":"e_1_3_1_181_2","doi-asserted-by":"publisher","DOI":"10.1109\/IWBF49977.2020.9107956"},{"key":"e_1_3_1_182_2","doi-asserted-by":"publisher","DOI":"10.1145\/2812802"},{"key":"e_1_3_1_183_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Torralba Antonio","year":"2011","unstructured":"Antonio Torralba and Alexei A. Efros. 2011. Unbiased look at dataset bias. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_184_2","doi-asserted-by":"publisher","DOI":"10.1162\/jocn.1991.3.1.71"},{"key":"e_1_3_1_185_2","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-020-03187-3"},{"key":"e_1_3_1_186_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00281"},{"key":"e_1_3_1_187_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2001.937709"},{"key":"e_1_3_1_188_2","volume-title":"Beitr\u00e4ge zur V\u00f6lkerkunde Der Deutschen Schutzgebiete","author":"Luschan Felix von","year":"1897","unstructured":"Felix von Luschan. 1897. Beitr\u00e4ge zur V\u00f6lkerkunde Der Deutschen Schutzgebiete. D. Reimer."},{"key":"e_1_3_1_189_2","doi-asserted-by":"publisher","DOI":"10.1109\/30.125072"},{"key":"e_1_3_1_190_2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2018.2822810"},{"key":"e_1_3_1_191_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Wang Hao","year":"2018","unstructured":"Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Dihong Gong, Jingchao Zhou, Zhifeng Li, and Wei Liu. 2018. Cosface: Large margin cosine loss for deep face recognition. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_192_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Wang Mei","year":"2020","unstructured":"Mei Wang and Weihong Deng. 2020. Mitigating bias in face recognition using skewness-aware reinforcement learning. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_193_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.10.081"},{"key":"e_1_3_1_194_2","volume-title":"Int. Conf. Comput. Vis.","author":"Wang Mei","year":"2019","unstructured":"Mei Wang, Weihong Deng, Jiani Hu, Xunqiang Tao, and Yaohai Huang. 2019. Racial faces in-the-wild: Reducing racial bias by information maximization adaptation network. In Int. Conf. Comput. Vis."},{"key":"e_1_3_1_195_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3103191"},{"key":"e_1_3_1_196_2","volume-title":"Proc. of the AAAI Conf. on Artificial Intelligence","author":"Wang Xiaobo","year":"2020","unstructured":"Xiaobo Wang, Shifeng Zhang, Shuo Wang, Tianyu Fu, Hailin Shi, and Tao Mei. 2020. Mis-classified vector guided softmax loss for face recognition. In Proc. of the AAAI Conf. on Artificial Intelligence."},{"key":"e_1_3_1_197_2","author":"Wee Sui-Lee","unstructured":"Sui-Lee Wee. 2019. China uses DNA to track its people, with the help of American expertise. The New York Times 21 (2019), 2019. Retrieved 2024-11-01 from https:\/\/www.nytimes.com\/2019\/02\/21\/business\/china-xinjiang-uighur-dna-thermo-fisher.html","journal-title":"The New York Times"},{"key":"e_1_3_1_198_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.87"},{"key":"e_1_3_1_199_2","volume-title":"Conf. on Learning Theory","author":"Woodworth Blake","year":"2017","unstructured":"Blake Woodworth, Suriya Gunasekar, Mesrob Ohannessian, and Nathan Srebro. 2017. Learning non-discriminatory predictors. In Conf. on Learning Theory."},{"key":"e_1_3_1_200_2","doi-asserted-by":"publisher","DOI":"10.11169\/bioimages.28.1"},{"key":"e_1_3_1_201_2","volume-title":"IEEE Int. Conf. on Big Data","author":"Xu Depeng","year":"2018","unstructured":"Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu. 2018. FairGAN: Fairness-aware GANs. In IEEE Int. Conf. on Big Data."},{"key":"e_1_3_1_202_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Xu Xingkun","year":"2021","unstructured":"Xingkun Xu, Yuge Huang, Pengcheng Shen, Shaoxin Li, Jilin Li, Feiyue Huang, Yong Li, and Zhen Cui. 2021. Consistent instance false positive improves fairness in face recognition. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_203_2","volume-title":"Int. Conf. on Artificial Intelligence and Statist.","author":"Xue Songkai","year":"2020","unstructured":"Songkai Xue, Mikhail Yurochkin, and Yuekai Sun. 2020. Auditing ML models for individual bias and unfairness. In Int. Conf. on Artificial Intelligence and Statist."},{"key":"e_1_3_1_204_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Yang Shuo","year":"2016","unstructured":"Shuo Yang, Ping Luo, Chen Loy, and Xiaoou Tang. 2016. WIDER FACE: A face detection benchmark. In IEEE Conf. Comput. Vis. Pattern Recog."},{"key":"e_1_3_1_205_2","volume-title":"IEEE Int. Joint Conf. on Biometrics (IJCB)","author":"Yang Zhanjia","year":"2021","unstructured":"Zhanjia Yang, Xiangping Zhu, Changyuan Jiang, Wenshuang Liu, and Linlin Shen. 2021. RamFace: Race adaptive margin based face recognition for racial bias mitigation. In IEEE Int. Joint Conf. on Biometrics (IJCB). IEEE."},{"key":"e_1_3_1_206_2","unstructured":"Dong Yi Zhen Lei Shengcai Liao and Stan Z. Li. 2014. Learning Face Representation from Scratch. 1411.7923 [cs.CV] https:\/\/arxiv.org\/abs\/1411.7923"},{"key":"e_1_3_1_207_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog.","author":"Yin Xi","year":"2019","unstructured":"Xi Yin, Xiang Yu, Kihyuk Sohn, Xiaoming Liu, and Manmohan Chandraker. 2019. Feature transfer learning for face recognition with under-represented data. In IEEE Conf. Comput. Vis. Pattern Recog.."},{"key":"e_1_3_1_208_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIA.2005.1635116"},{"key":"e_1_3_1_209_2","volume-title":"IEEE Conf. Comput. Vis. Pattern Recog. Worksh.","author":"Yucer Seyma","year":"2020","unstructured":"Seyma Yucer, Samet Ak\u00e7ay, Noura Al-Moubayed, and Toby P. Breckon. 2020. Exploring racial bias within face recognition via per-subject adversarially-enabled data augmentation. In IEEE Conf. Comput. Vis. Pattern Recog. Worksh."},{"key":"e_1_3_1_210_2","volume-title":"IEEE Int. Joint Conf. on Biometrics","author":"Yucer Seyma","year":"2022","unstructured":"Seyma Yucer, Matt Poyser, Noura Al-Moubayed, and Toby P. Breckon. 2022. Does lossy image compression affect racial bias within face recognition?. In IEEE Int. Joint Conf. on Biometrics."},{"key":"e_1_3_1_211_2","volume-title":"IEEE Winter Conf. on Applications of Comput. Vis.","author":"Yucer Seyma","year":"2022","unstructured":"Seyma Yucer, Furkan Tektas, Noura Al Moubayed, and Toby P. Breckon. 2022. Measuring hidden bias within face recognition via racial phenotypes. In IEEE Winter Conf. on Applications of Comput. Vis."},{"key":"e_1_3_1_212_2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2016.2603342"},{"key":"e_1_3_1_213_2","author":"Zhang Zhifei","unstructured":"Zhifei Zhang, Yang Song, and Hairong Qi. 2017. Age progression\/regression by conditional adversarial autoencoder. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"e_1_3_1_214_2","unstructured":"Erjin Zhou Zhimin Cao and Qi Yin. 2015. Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not? 1501.04690 [cs.CV] https:\/\/arxiv.org\/abs\/1501.04690"},{"key":"e_1_3_1_215_2","unstructured":"Yanjia Zhu Hongxiang Cai Shuhan Zhang Chenhao Wang and Yichao Xiong. 2021. TinaFace: Strong but Simple Baseline for Face Detection. 2011.13183 [cs.CV] https:\/\/arxiv.org\/abs\/2011.13183"},{"key":"e_1_3_1_216_2","doi-asserted-by":"publisher","DOI":"10.1093\/annhyg\/meq007"},{"key":"e_1_3_1_217_2","volume-title":"Thicker than Blood: How Racial Statistics Lie","author":"Zuberi Tukufu","year":"2001","unstructured":"Tukufu Zuberi. 2001. Thicker than Blood: How Racial Statistics Lie. U of Minnesota Press."},{"key":"e_1_3_1_218_2","unstructured":"Tukufu Zuberi and Eduardo Bonilla-Silva. 2008. White Logic White Methods: Racism and Methodology. Rowman & Littlefield Publishers."}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3705295","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3705295","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:02Z","timestamp":1750295882000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3705295"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,23]]},"references-count":217,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4,30]]}},"alternative-id":["10.1145\/3705295"],"URL":"https:\/\/doi.org\/10.1145\/3705295","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,23]]},"assertion":[{"value":"2023-03-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}