{"created":"2025-01-19T01:15:19.961084+00:00","updated":"2025-01-19T16:35:46.947284+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00214518","sets":["6164:6165:6462:10749"]},"path":["10749"],"owner":"44499","recid":"214518","title":["顔認証における公平性評価の一検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-10-19"},"_buckets":{"deposit":"5ab86434-e86b-422e-89a4-ec4391ff6012"},"_deposit":{"id":"214518","pid":{"type":"depid","value":"214518","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"顔認証における公平性評価の一検討","author_link":["551182","551184","551181","551183"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"顔認証における公平性評価の一検討"},{"subitem_title":"A Study on Fairness Evaluation for Face Recognition Systems","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"顔認証，公平性，生体認証","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2021-10-19","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"静岡大学／理化学研究所革新知能統合研究センター"},{"subitem_text_value":"理化学研究所革新知能統合研究センター"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Shizuoka University / RIKEN AIP","subitem_text_language":"en"},{"subitem_text_value":"RIKEN AIP","subitem_text_language":"en"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/214518/files/IPSJCSS2021118.pdf","label":"IPSJCSS2021118.pdf"},"date":[{"dateType":"Available","dateValue":"2023-10-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJCSS2021118.pdf","filesize":[{"value":"633.9 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"30"},{"tax":["include_tax"],"price":"0","billingrole":"46"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"99584442-4ea3-4260-b21f-cb58c32ee216","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"大木, 哲史"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"荒井, ひろみ"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tetsushi, Ohki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiromi, Arai","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"顔認証技術は，特に欧米において犯罪捜査を目的とした利用が進んでいるが，一方で近年，学習データの偏りによって，有色人種や性別などのセンシティブ情報に依存して顔認証技術の精度が大きく変動することが社会問題となりつつある．本研究では，顔認証における公平性について取り上げる．例えば犯罪者リストとの照合など人生に重大な影響を与える用途において．ある特定のセンシティブ情報をもつグループが犯罪者に誤認識されやすいような状況は不公平であると言える．本研究ではこのような不公平さについて，特に表現学習に基づく汎用的な顔認証システムを取り上げ，その公平性評価方法について検討，評価を行った．MORPH データセットを用いた実験により，デモグラフィックに偏りのある学習セットにより作成された顔特徴抽出器を用いた場合に，認証精度の偏りが生じることや，偏りのあるテストセットを用いることで意図的に高い公平性評価値を算出可能である，属性ごとのしきい値を設定することで認証精度への影響を軽減できることなどが示された．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Face recognition technology is increasingly being used for criminal investigations, especially in Europe and the United States. However, it has become a social problem that the accuracy of face recognition technology varies greatly depending on sensitive information such as race and gender due to training data bias. For example, in a life-critical application such as matching with a criminal list, it is unfair that criminals easily misrecognize a group with certain sensitive information. This paper discusses and evaluates the fairness evaluation method of a general-purpose face recognition system based on representation learning. The MORPH dataset experiments showed that authentication accuracy bias occurs when using a face feature extractor created with a demographically biased training set. Furthermore, we showed that it is possible to intentionally calculate a high fairness evaluation value using a biased test set. The effect on authentication accuracy can be reduced by setting a threshold value for each attribute.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"886","bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2021論文集"}],"bibliographicPageStart":"879","bibliographicIssueDates":{"bibliographicIssueDate":"2021-10-19","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":214518,"links":{}}