{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T12:32:07Z","timestamp":1743078727140,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030613761"},{"type":"electronic","value":"9783030613778"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-61377-8_20","type":"book-chapter","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T19:04:06Z","timestamp":1602788646000},"page":"287-301","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving Face Recognition Accuracy for Brazilian Faces in a Criminal Investigation Department"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1262-565X","authenticated-orcid":false,"given":"Jones Jos\u00e9","family":"da Silva J\u00fanior","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2967-6077","authenticated-orcid":false,"given":"Anderson Silva","family":"Soares","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,13]]},"reference":[{"key":"20_CR1","unstructured":"Institute of Automation, C.A.o.C.: CASIA WebFAce. http:\/\/www.cbsr.ia.ac.cn\/english\/CASIA-WebFace-Database.html. Acessado em abril de 2019"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: VGGFace2: a dataset for recognising faces across pose and age (2017)","DOI":"10.1109\/FG.2018.00020"},{"key":"20_CR3","unstructured":"de Geografia e Estat\u00edstica, I.B.: Censo demogr\u00e1fico do brasil. https:\/\/sidra.ibge.gov.br\/Tabela\/#resultado (2010). Acessado em 23 Mar 2020"},{"key":"20_CR4","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":"20_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-3-642-04391-8_3","volume-title":"Biometric ID Management and Multimodal Communication","author":"AW Rawls","year":"2009","unstructured":"Rawls, A.W., Ricanek, K.: MORPH: development and optimization of a longitudinal age progression database. In: Fierrez, J., Ortega-Garcia, J., Esposito, A., Drygajlo, A., Faundez-Zanuy, M. (eds.) BioID 2009. LNCS, vol. 5707, pp. 17\u201324. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04391-8_3"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Klar, B.F., et al.: Pushing the frontiers of unconstrained face detection and recognition: iarpa janus benchmark A (2015)","DOI":"10.1109\/CVPR.2015.7298803"},{"key":"20_CR7","doi-asserted-by":"publisher","first-page":"12661","DOI":"10.1038\/s41598-018-31129-7","volume":"8","author":"ME Nicholls","year":"2018","unstructured":"Nicholls, M.E., Churches, O., Loetscher, T.: Perception of an ambiguous figure is affected by own-age social biases. Sci. Rep. 8, 12661 (2018)","journal-title":"Sci. Rep."},{"key":"20_CR8","unstructured":"Merler, M., Ratha, N., Feris, R.S., Smith, J.R.: Diversity in faces (2019)"},{"key":"20_CR9","unstructured":"Mitchell, T.M.: The need for biases in learning generalizations. Laboratory for Computer Science Research, Department of Computer Science (1980)"},{"key":"20_CR10","unstructured":"Nagpal, S., Singh, M., Singh, R., Vatsa, M., Ratha, N.: Deep learning for face recognition: pride or prejudiced? (2019)"},{"issue":"1","key":"20_CR11","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","volume":"12","author":"N Qian","year":"1999","unstructured":"Qian, N.: On the momentum term in gradient descent learning algorithms. Neural Netw. Official J. Int. Neural Netw. Soc. 12(1), 145\u2013151 (1999)","journal-title":"Neural Netw. Official J. Int. Neural Netw. Soc."},{"key":"20_CR12","unstructured":"Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. Image Vis. Comput. 28, 807\u2013813 (2010)"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Raji, I.D., Buolamwini, J.: Actionable auditing: investigating the impact of publicly naming biased performance results of commercial AI products. In: AAAI ACM Conference on AI Ethics and Society (2019)","DOI":"10.1145\/3306618.3314244"},{"key":"20_CR14","unstructured":"Rio, G.: Sistema de reconhecimento facial da pm do rj falha, e mulher \u00e9 detida por engano (2019). https:\/\/g1.globo.com\/rj\/rio-de-janeiro\/noticia\/2019\/07\/11\/sistema-de-reconhecimento-facial-da-pm-do-rj-falha-e-mulher-e-detida-por-engano.ghtml. Accessed 27 May 2020"},{"key":"20_CR15","unstructured":"Sandberg, D.: Face recognition using Tensorflow. https:\/\/github.com\/davidsandberg\/facenet. Acessado em 01 Apr 2020"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-resnet and the impact of residual connections on learning (2016)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"20_CR18","unstructured":"The New York Times: How the police use facial recognition, and where it falls short (2020). https:\/\/www.nytimes.com\/2020\/01\/12\/technology\/facial-recognition-police.html. Acessado em 04 Apr 2020"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Wang, F., Liu, W., Liu, H., Cheng, J.: Additive margin softmax for face verification (2018)","DOI":"10.1109\/LSP.2018.2822810"},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Wang, M., Deng, W.: Deep face recognition: a survey. arXiv preprint arXiv:1804.06655 (2019)","DOI":"10.1016\/j.neucom.2020.10.081"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Wang, M., Deng, W., Hu, J., Tao, X., Huang, Y.: Racial faces in-the-wild: reducing racial bias by information maximization adaptation network (2018)","DOI":"10.1109\/ICCV.2019.00078"},{"key":"20_CR22","unstructured":"Weinberger, K.Q., Blitzer, J., Saul., L.K.: Distance metric learning for large margin nearest neighbor classification. MIT Press (2011)"},{"key":"20_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/978-3-319-46478-7_31","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Y Wen","year":"2016","unstructured":"Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499\u2013515. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_31"},{"key":"20_CR24","unstructured":"Wu, X., He, R., Sun, Z., Tan, T.: A light CNN for deep face representation with noisy labels (2015)"},{"issue":"10","key":"20_CR25","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). https:\/\/doi.org\/10.1109\/LSP.2016.2603342","journal-title":"IEEE Sig. Process. Lett."},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Zhuang, F., et al.: A comprehensive survey on transfer learning (2019)","DOI":"10.1109\/JPROC.2020.3004555"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61377-8_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T13:46:34Z","timestamp":1669211194000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-61377-8_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030613761","9783030613778"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61377-8_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"13 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rio Grande","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www2.sbc.org.br\/bracis2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"JEMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"228","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the Corona pandemic BRACIS 2020 was held as a virtual event.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}