{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T21:59:58Z","timestamp":1742939998051,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031377303"},{"type":"electronic","value":"9783031377310"}],"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\/978-3-031-37731-0_39","type":"book-chapter","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T14:02:30Z","timestamp":1691589750000},"page":"536-550","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Using PGAN to\u00a0Create Synthetic Face Images to\u00a0Reduce Bias in\u00a0Biometric Systems"],"prefix":"10.1007","author":[{"given":"Andrea","family":"Bozzitelli","sequence":"first","affiliation":[]},{"given":"Pia","family":"Cavasinni di Benedetto","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1391-8502","authenticated-orcid":false,"given":"Maria","family":"De Marsico","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"39_CR1","series-title":"Advances in Computer Vision and Pattern Recognition","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/978-3-030-74697-1_15","volume-title":"Deep Learning-Based Face Analytics","author":"G Balakrishnan","year":"2021","unstructured":"Balakrishnan, G., Xiong, Y., Xia, W., Perona, P.: Towards causal benchmarking of biasin face analysis algorithms. In: Ratha, N.K., Patel, V.M., Chellappa, R. (eds.) Deep Learning-Based Face Analytics. ACVPR, pp. 327\u2013359. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-74697-1_15"},{"key":"39_CR2","unstructured":"European Commission: High-Level Expert Group on AI. Ethics guidelines for trustworthy AI (2019). https:\/\/digital-strategy.ec.europa.eu\/en\/library\/ethics-guidelines-trustworthy-ai. Accessed 20 July 2023"},{"issue":"1","key":"39_CR3","first-page":"2","volume":"40","author":"CM Cook","year":"2019","unstructured":"Cook, C.M., Howard, J.J., Sirotin, Y.B., Tipton, J.L.: Fixed and varying effects of demographic factors on the performance of eleven commercial facial recognition systems. IEEE Trans. Biom. Behav. Identity Sci. 40(1), 2 (2019)","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"issue":"22","key":"39_CR4","doi-asserted-by":"publisher","first-page":"23383","DOI":"10.1007\/s11042-016-4085-8","volume":"76","author":"M De Marsico","year":"2017","unstructured":"De Marsico, M., Nappi, M., Riccio, D., Wechsler, H.: Leveraging implicit demographic information for face recognition using a multi-expert system. Multimedia Tools Appl. 76(22), 23383\u201323411 (2017)","journal-title":"Multimedia Tools Appl."},{"issue":"3","key":"39_CR5","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1109\/TBIOM.2021.3082038","volume":"3","author":"X Di","year":"2021","unstructured":"Di, X., Patel, V.M.: Multimodal face synthesis from visual attributes. IEEE Trans. Biom. Behav. Identity Sci. 3(3), 427\u2013439 (2021)","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"issue":"2","key":"39_CR6","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1109\/TTS.2020.2992344","volume":"1","author":"P Drozdowski","year":"2020","unstructured":"Drozdowski, P., Rathgeb, C., Dantcheva, A., Damer, N., Busch, C.: Demographic bias in biometrics: a survey on an emerging challenge. IEEE Trans. Technol. Soc. 1(2), 89\u2013103 (2020)","journal-title":"IEEE Trans. Technol. Soc."},{"key":"39_CR7","doi-asserted-by":"crossref","unstructured":"Garcia, R.V., Wandzik, L., Grabner, L., Krueger, J.: The harms of demographic bias in deep face recognition research. In: 2019 International Conference on Biometrics (ICB), pp. 1\u20136. IEEE (2019)","DOI":"10.1109\/ICB45273.2019.8987334"},{"key":"39_CR8","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol. 27. Curran Associates, Inc. (2014). https:\/\/proceedings.neurips.cc\/paper\/2014\/file\/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf"},{"key":"39_CR9","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems 30 (2017)"},{"key":"39_CR10","unstructured":"Hinton, G., Srivastava, N., Swersky, K.: Neural Networks for Machine Learning - Lecture 6e - rmsprop: Divide the gradient by a running average of its recent magnitude. https:\/\/www.cs.toronto.edu\/~tijmen\/csc321\/slides\/lecture_slides_lec6.pdf. Accessed 05 June 2022"},{"key":"39_CR11","doi-asserted-by":"crossref","unstructured":"Howard, J.J., Blanchard, A.J., Sirotin, Y.B., Hasselgren, J.A., Vemury, A.R.: An investigation of high-throughput biometric systems: results of the 2018 department of homeland security biometric technology rally. In: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1\u20137. IEEE (2018)","DOI":"10.1109\/BTAS.2018.8698547"},{"key":"39_CR12","doi-asserted-by":"crossref","unstructured":"Hu, S., et al.: A polarimetric thermal database for face recognition research. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 119\u2013126 (2016)","DOI":"10.1109\/CVPRW.2016.30"},{"key":"39_CR13","unstructured":"Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: International Conference on Learning Representations (2018)"},{"issue":"6","key":"39_CR14","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1109\/TIFS.2012.2214212","volume":"7","author":"BF Klare","year":"2012","unstructured":"Klare, B.F., Burge, M.J., Klontz, J.C., Bruegge, R.W.V., Jain, A.K.: Face recognition performance: role of demographic information. IEEE Trans. Inf. Forensics Secur. 7(6), 1789\u20131801 (2012)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"39_CR15","doi-asserted-by":"crossref","unstructured":"Li, S., Yi, D., Lei, Z., Liao, S.: The CASIA NIR-VIS 2.0 face database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 348\u2013353 (2013)","DOI":"10.1109\/CVPRW.2013.59"},{"issue":"1","key":"39_CR16","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/TBIOM.2018.2890577","volume":"1","author":"B Lu","year":"2019","unstructured":"Lu, B., Chen, J.C., Castillo, C.D., Chellappa, R.: An experimental evaluation of covariates effects on unconstrained face verification. IEEE Trans. Biom. Behav. Identity Sci. 1(1), 42\u201355 (2019)","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"39_CR17","doi-asserted-by":"crossref","unstructured":"Ngan, M., Grother, P.J., Ngan, M.: Face recognition vendor test (FRVT) performance of automated gender classification algorithms. US Department of Commerce, National Institute of Standards and Technology (2015)","DOI":"10.6028\/NIST.IR.8052"},{"key":"39_CR18","unstructured":"Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: International Conference on Machine Learning, pp. 2642\u20132651. PMLR (2017)"},{"issue":"7","key":"39_CR19","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","volume":"24","author":"T Ojala","year":"2002","unstructured":"Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971\u2013987 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"39_CR20","unstructured":"Perarnau, G., Van De Weijer, J., Raducanu, B., \u00c1lvarez, J.M.: Invertible conditional GANs for image editing. arXiv preprint arXiv:1611.06355 (2016)"},{"issue":"2","key":"39_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1870076.1870082","volume":"8","author":"PJ Phillips","year":"2011","unstructured":"Phillips, P.J., Jiang, F., Narvekar, A., Ayyad, J., O\u2019Toole, A.J.: An other-race effect for face recognition algorithms. ACM Trans. Appl. Percept. (TAP) 8(2), 1\u201311 (2011)","journal-title":"ACM Trans. Appl. Percept. (TAP)"},{"issue":"5","key":"39_CR22","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1109\/TPAMI.2009.59","volume":"32","author":"PJ Phillips","year":"2010","unstructured":"Phillips, P.J., et al.: FRVT 2006 and ICE 2006 large-scale experimental results. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 831\u2013846 (2010). https:\/\/doi.org\/10.1109\/TPAMI.2009.59","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"39_CR23","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/s10044-006-0033-y","volume":"9","author":"L Shen","year":"2006","unstructured":"Shen, L., Bai, L.: A review on gabor wavelets for face recognition. Pattern Anal. Appl. 9(2), 273\u2013292 (2006)","journal-title":"Pattern Anal. Appl."},{"key":"39_CR24","doi-asserted-by":"crossref","unstructured":"Srinivas, N., Atwal, H., Rose, D.C., Mahalingam, G., Ricanek, K., Bolme, D.S.: Age, gender, and fine-grained ethnicity prediction using convolutional neural networks for the East Asian face dataset. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 953\u2013960. IEEE (2017)","DOI":"10.1109\/FG.2017.118"},{"key":"39_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Y., Dantcheva, A., Bremond, F.: From attributes to faces: a conditional generative network for face generation. In: 2018 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1\u20135. IEEE (2018)","DOI":"10.23919\/BIOSIG.2018.8553377"},{"key":"39_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1007\/978-3-319-46493-0_47","volume-title":"Computer Vision \u2013 ECCV 2016","author":"X Yan","year":"2016","unstructured":"Yan, X., Yang, J., Sohn, K., Lee, H.: Attribute2Image: conditional image generation from visual attributes. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 776\u2013791. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_47"},{"key":"39_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Song, Y., Qi, H.: Age progression\/regression by conditional adversarial autoencoder. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5810\u20135818 (2017)","DOI":"10.1109\/CVPR.2017.463"}],"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\/978-3-031-37731-0_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T14:08:08Z","timestamp":1691590088000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-37731-0_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031377303","9783031377310"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-37731-0_39","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":"10 August 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"}}]}}