{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:51:23Z","timestamp":1742935883623,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031376597"},{"type":"electronic","value":"9783031376603"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-37660-3_32","type":"book-chapter","created":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T06:02:20Z","timestamp":1690610540000},"page":"448-464","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Disparate Impact in\u00a0Facial Recognition Stems from\u00a0the\u00a0Broad Homogeneity Effect: A Case Study and\u00a0Method to\u00a0Resolve"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0136-9865","authenticated-orcid":false,"given":"John J.","family":"Howard","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0668-8745","authenticated-orcid":false,"given":"Eli J.","family":"Laird","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7058-0424","authenticated-orcid":false,"given":"Yevgeniy B.","family":"Sirotin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"key":"32_CR1","unstructured":"Rally - Maryland Test Facility. https:\/\/mdtf.org\/Rally2021"},{"key":"32_CR2","doi-asserted-by":"publisher","unstructured":"An, X., et al.: Partial FC: training 10 million identities on a single machine. In: 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 1445\u20131449 (2021). https:\/\/doi.org\/10.1109\/ICCVW54120.2021.00166","DOI":"10.1109\/ICCVW54120.2021.00166"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Boutros, F., Damer, N., Kirchbuchner, F., Kuijper, A.: ElasticFace: elastic margin loss for deep face recognition. CoRR abs\/2109.09416 (2021). https:\/\/arxiv.org\/abs\/2109.09416","DOI":"10.1109\/CVPRW56347.2022.00164"},{"key":"32_CR4","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Niannan, X., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"32_CR5","doi-asserted-by":"crossref","unstructured":"Drozdowski, P., Rathgeb, C., Busch, C.: The watchlist imbalance effect in biometric face identification: comparing theoretical estimates and empiric measurements. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3757\u20133765 (2021)","DOI":"10.1109\/ICCVW54120.2021.00419"},{"key":"32_CR6","unstructured":"Duta, I.C., Liu, L., Zhu, F., Shao, L.: Improved residual networks for image and video recognition. arXiv preprint arXiv:2004.04989 (2020)"},{"key":"32_CR7","unstructured":"Gini, C.: Variabilit\u00e0 e mutabilit\u00e0. Reprinted in Memorie di metodologica statistica (Ed. Pizetti E) (1912)"},{"key":"32_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1007\/978-3-030-58526-6_20","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Gong","year":"2020","unstructured":"Gong, S., Liu, X., Jain, A.K.: Jointly de-biasing face recognition and demographic attribute estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 330\u2013347. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_20"},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Grother, P.: Face recognition vendor test (FRVT) part 8: summarizing demographic differentials (2022)","DOI":"10.6028\/NIST.IR.8429.ipd"},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Grother, P., Ngan, M., Hanaoka, K.: Face recognition vendor test (FRVT) part 2: identification (2018)","DOI":"10.6028\/NIST.IR.8238"},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"Grother, P., Ngan, M., Hanaoka, K.: Face recognition vendor test (FRVT) part 3: demographic effects (2019)","DOI":"10.6028\/NIST.IR.8280"},{"key":"32_CR12","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. LNCS, vol. 9907, pp. 87\u2013102. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_6"},{"key":"32_CR13","unstructured":"Hasselgren, J.A., Howard, J.J., Sirotin, Y.B., Tipton, J.L., Vemury, A.R.: A scenario evaluation of high-throughput face biometric systems: select results from the 2019 Department of Homeland Security Biometric Technology Rally. The Maryland Test Facility (2020)"},{"key":"32_CR14","doi-asserted-by":"crossref","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), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"32_CR15","doi-asserted-by":"publisher","unstructured":"Howard, J.J., Sirotin, Y.B., Vemury, A.R.: The effect of broad and specific demographic homogeneity on the imposter distributions and false match rates in face recognition algorithm performance. In: 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1\u20138 (2019). https:\/\/doi.org\/10.1109\/BTAS46853.2019.9186002","DOI":"10.1109\/BTAS46853.2019.9186002"},{"key":"32_CR16","doi-asserted-by":"publisher","unstructured":"Howard, J.J., Laird, E.J., Sirotin, Y.B., Rubin, R.E., Tipton, J.L., Vemury, A.R.: Evaluating proposed fairness models for face recognition algorithms (2022). https:\/\/doi.org\/10.48550\/ARXIV.2203.05051. https:\/\/arxiv.org\/abs\/2203.05051","DOI":"10.48550\/ARXIV.2203.05051"},{"key":"32_CR17","unstructured":"Howard, J.J., Sirotin, Y.B., Tipton, J.L., Vemury, A.R.: Quantifying the extent to which race and gender features determine identity in commercial face recognition algorithms (2020)"},{"key":"32_CR18","unstructured":"Huang, G., Mattar, M.A., Berg, T.L., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments (2008)"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"Huang, Y., et al.: CurricularFace: adaptive curriculum learning loss for deep face recognition. CoRR abs\/2004.00288 (2020). https:\/\/arxiv.org\/abs\/2004.00288","DOI":"10.1109\/CVPR42600.2020.00594"},{"key":"32_CR20","unstructured":"InsightFace: State-of-the-art 2D and 3D face analysis project. https:\/\/github.com\/deepinsight\/insightface\/tree\/master\/model_zoo"},{"key":"32_CR21","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: SphereFace: deep hypersphere embedding for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 212\u2013220 (2017)","DOI":"10.1109\/CVPR.2017.713"},{"key":"32_CR22","unstructured":"Manaher, C.: Privacy impact assessment for the traveler verification service (2018). https:\/\/www.dhs.gov\/publication\/dhscbppia-056-traveler-verification-service"},{"key":"32_CR23","doi-asserted-by":"publisher","unstructured":"de Freitas Pereira, T., Marcel, S.: Fairness in biometrics: a figure of merit to assess biometric verification systems. IEEE Trans. Biometrics Behav. Identity Sci 4, 19\u201329 (2021). https:\/\/doi.org\/10.1109\/TBIOM.2021.3102862","DOI":"10.1109\/TBIOM.2021.3102862"},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press (2011)","DOI":"10.1017\/CBO9781139058452"},{"key":"32_CR25","doi-asserted-by":"publisher","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07\u201312-June, pp. 815\u2013823 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298682","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"32_CR26","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701\u20131708 (2014)","DOI":"10.1109\/CVPR.2014.220"},{"key":"32_CR27","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5265\u20135274 (2018)","DOI":"10.1109\/CVPR.2018.00552"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-37660-3_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T06:08:30Z","timestamp":1690610910000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-37660-3_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031376597","9783031376603"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-37660-3_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montr\u00e9al, QC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iapr.org\/icpr2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}