AdaFace: Quality Adaptive Margin for Face Recognition
Recognition in low quality face datasets is challenging because facial attributes are obscured and degraded. Advances in margin-based loss functions have resulted in enhanced discriminability of faces in the embedding space. Further, previous studies have studied the effect of adaptive losses to ass...
Gespeichert in:
| Veröffentlicht in: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 18729 - 18738 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
01.06.2022
|
| Schlagworte: | |
| ISSN: | 1063-6919 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Recognition in low quality face datasets is challenging because facial attributes are obscured and degraded. Advances in margin-based loss functions have resulted in enhanced discriminability of faces in the embedding space. Further, previous studies have studied the effect of adaptive losses to assign more importance to misclassified (hard) examples. In this work, we introduce another aspect of adaptiveness in the loss function, namely the image quality. We argue that the strategy to emphasize misclassified samples should be adjusted according to their image quality. Specifically, the relative importance of easy or hard samples should be based on the sample's image quality. We propose a new loss function that emphasizes samples of different difficulties based on their image quality. Our method achieves this in the form of an adaptive margin function by approximating the image quality with feature norms. Extensive experiments show that our method, AdaFace, improves the face recognition performance over the state-of-the-art (SoTA) on four datasets (IJB-B, IJB-C, IJB-S and TinyFace). Code and models are released in Supp. |
|---|---|
| AbstractList | Recognition in low quality face datasets is challenging because facial attributes are obscured and degraded. Advances in margin-based loss functions have resulted in enhanced discriminability of faces in the embedding space. Further, previous studies have studied the effect of adaptive losses to assign more importance to misclassified (hard) examples. In this work, we introduce another aspect of adaptiveness in the loss function, namely the image quality. We argue that the strategy to emphasize misclassified samples should be adjusted according to their image quality. Specifically, the relative importance of easy or hard samples should be based on the sample's image quality. We propose a new loss function that emphasizes samples of different difficulties based on their image quality. Our method achieves this in the form of an adaptive margin function by approximating the image quality with feature norms. Extensive experiments show that our method, AdaFace, improves the face recognition performance over the state-of-the-art (SoTA) on four datasets (IJB-B, IJB-C, IJB-S and TinyFace). Code and models are released in Supp. |
| Author | Liu, Xiaoming Kim, Minchul Jain, Anil K. |
| Author_xml | – sequence: 1 givenname: Minchul surname: Kim fullname: Kim, Minchul email: kimminc2@cse.msu.edu organization: Michigan State University,Department of Computer Science and Engineering,East Lansing,MI,48824 – sequence: 2 givenname: Anil K. surname: Jain fullname: Jain, Anil K. email: jain@cse.msu.edu organization: Michigan State University,Department of Computer Science and Engineering,East Lansing,MI,48824 – sequence: 3 givenname: Xiaoming surname: Liu fullname: Liu, Xiaoming email: liuxm@cse.msu.edu organization: Michigan State University,Department of Computer Science and Engineering,East Lansing,MI,48824 |
| BookMark | eNotjNtKw0AURUdRsK39An2YH0h6zsxkLr6VYLVQUYv6WuZaRmpSkljo31vRpw2LtfaYXDRtEwm5RSgRwczqj5d1xaTWJQPGSkCN5oyMUcpKSCMkPycjBMkLadBckWnffwIAZ4jS6BGp5sEurI939PXb7vJwpCewH_Ih0ifbbXNDU9vRX4Ouo2-3TR5y21yTy2R3fZz-74S8L-7f6sdi9fywrOerIjPgQxGsrqRQLLoKNHrjHQsBE3jFRHJBSMcVj8J47ThwndAlxblxYIMJSkU-ITd_vznGuNl3-ct2x43RGtgp-AEzwUdR |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IH CBEJK RIE RIO |
| DOI | 10.1109/CVPR52688.2022.01819 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan (POP) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE/IET Electronic Library IEEE Proceedings Order Plans (POP) 1998-present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences |
| EISBN | 1665469463 9781665469463 |
| EISSN | 1063-6919 |
| EndPage | 18738 |
| ExternalDocumentID | 9880230 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IH 6IL 6IN AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IJVOP OCL RIE RIL RIO |
| ID | FETCH-LOGICAL-i203t-da856472eb5081c9cb2dd1f0c724fbd46b373e49c8b3038f1bf7339b0ad9d77e3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 344 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000870783004054&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 02:15:10 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i203t-da856472eb5081c9cb2dd1f0c724fbd46b373e49c8b3038f1bf7339b0ad9d77e3 |
| PageCount | 10 |
| ParticipantIDs | ieee_primary_9880230 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-June |
| PublicationDateYYYYMMDD | 2022-06-01 |
| PublicationDate_xml | – month: 06 year: 2022 text: 2022-June |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) |
| PublicationTitleAbbrev | CVPR |
| PublicationYear | 2022 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0003211698 |
| Score | 2.660758 |
| Snippet | Recognition in low quality face datasets is challenging because facial attributes are obscured and degraded. Advances in margin-based loss functions have... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 18729 |
| SubjectTerms | Adaptation models categorization Codes Computer vision Face and gestures; Recognition: detection Face recognition Image quality retrieval Training Training data |
| Title | AdaFace: Quality Adaptive Margin for Face Recognition |
| URI | https://ieeexplore.ieee.org/document/9880230 |
| WOSCitedRecordID | wos000870783004054&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB5q8eCpaiu-ycGj22aT3U3iTYrFg5RSVHoreUIv29KH4L83s7tWBC_ewhAITJjMI998A3BX-MCEsSEJ0rgk8y7aHDUhcVkmEFVoja5u-kWMx3I2U5MW3O97Ybz3FfjM93FZ_eW7pd1hqWygJNKVxQT9QIii7tXa11N4zGQKJZvuuJSqwfB9MkUyEwRwMdZHZir1a4ZK5UJGnf8dfgy9n148Mtl7mRNo-fIUOk3wSBrT3HQhf3R6pK1_IDUtxieJghU-ZgSH2S5KEsNTgjvI9Bs0tCx78DZ6eh0-J81MhGTBKN8mTsscGd-9iZFVapU1zLk0UCtYFozLCsMF95my0kTnJENqguBcGaqdckJ4fgbtcln6cyBcGloYTbXAJJEFpWxeuGjxUnIdZHoBXdTCfFXTXswbBVz-Lb6CI1RzjaK6hvZ2vfM3cGg_tovN-ra6qy-MZJRy |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB5KFfRUtRXf5uDRbXeT7SbxJsVSsZZSqvRW8oRetqUPwX9vZrtWBC_ewhAITJjMI998A3CXOU-5Nj7yQtsodTbYXKx9ZNOUI6rQaFXcdJ8PBmIykcMK3O96YZxzBfjMNXFZ_OXbudlgqawlBdKVhQR9Dydnld1au4oKC7lMJkXZH5fEstV5H46QzgQhXJQ2kZtK_pqiUjiRbu1_xx9B46cbjwx3fuYYKi4_gVoZPpLSOFd1aD9a1VXGPZAtMcYnCYIFPmcEx9nOchICVII7yOgbNjTPG_DWfRp3elE5FSGa0ZitI6tEGznfnQ6xVWKk0dTaxMeG09Rrm2aaceZSaYQO7kn4RHvOmNSxstJy7tgpVPN57s6AMKHjTKtYcUwTqZfStDMbbF4IprxIzqGOWpgutsQX01IBF3-Lb-GgN37tT_vPg5dLOESVbzFVV1BdLzfuGvbNx3q2Wt4U9_YFfOmXuw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=proceeding&rft.title=Proceedings+%28IEEE+Computer+Society+Conference+on+Computer+Vision+and+Pattern+Recognition.+Online%29&rft.atitle=AdaFace%3A+Quality+Adaptive+Margin+for+Face+Recognition&rft.au=Kim%2C+Minchul&rft.au=Jain%2C+Anil+K.&rft.au=Liu%2C+Xiaoming&rft.date=2022-06-01&rft.pub=IEEE&rft.eissn=1063-6919&rft.spage=18729&rft.epage=18738&rft_id=info:doi/10.1109%2FCVPR52688.2022.01819&rft.externalDocID=9880230 |