KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition
Feature learning is a widely used method for large-scale face recognition tasks. Recently, large-margin softmax loss methods have demonstrated significant improvements in deep face recognition. However, these methods typically propose fixed positive margins to enforce intra-class compactness and int...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10339282/ |
| _version_ | 1797393081336594432 |
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| author | Chingis Oinar Binh M. Le Simon S. Woo |
| author_facet | Chingis Oinar Binh M. Le Simon S. Woo |
| author_sort | Chingis Oinar |
| collection | DOAJ |
| description | Feature learning is a widely used method for large-scale face recognition tasks. Recently, large-margin softmax loss methods have demonstrated significant improvements in deep face recognition. However, these methods typically propose fixed positive margins to enforce intra-class compactness and inter-class diversity, without considering imbalanced learning issues that arise due to different learning difficulties or the number of training samples available in each class. This overlook not only compromises the efficiency of the learning process but, more critically, the generalization capability of the resultant models. To address this problem, we introduce a novel adaptive strategy called KappaFace, which modulates the relative importance of each class based on its learning difficulty and imbalance. Drawing inspiration from the von Mises-Fisher distribution, KappaFace increases the margin values for the challenging or underrepresented classes and decreases that of more well-represented classes. Comprehensive experiments across eight cutting-edge baselines and nine well-established facial benchmark datasets strongly confirm the advantage of our method. Notably, we observed an enhancement of up to 0.5% on the verification task when evaluated on the IJB-B/C datasets. In conclusion, KappaFace offers a novel solution that effectively tackles imbalanced learning in deep face recognition tasks and establishes a new baseline. |
| first_indexed | 2024-03-08T23:57:58Z |
| format | Article |
| id | doaj.art-a815777058f44bbc93b692ffc9677f44 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T23:57:58Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a815777058f44bbc93b692ffc9677f442023-12-13T00:00:49ZengIEEEIEEE Access2169-35362023-01-011113713813715010.1109/ACCESS.2023.333864810339282KappaFace: Adaptive Additive Angular Margin Loss for Deep Face RecognitionChingis Oinar0Binh M. Le1https://orcid.org/0000-0002-4344-3421Simon S. Woo2https://orcid.org/0000-0002-8983-1542Mercari, Tokyo, JapanDepartment of Computer Science and Engineering, College of Computing and Informatics, Sungkyunkwan University, Suwon, South KoreaDepartment of Computer Science and Engineering, College of Computing and Informatics, Sungkyunkwan University, Suwon, South KoreaFeature learning is a widely used method for large-scale face recognition tasks. Recently, large-margin softmax loss methods have demonstrated significant improvements in deep face recognition. However, these methods typically propose fixed positive margins to enforce intra-class compactness and inter-class diversity, without considering imbalanced learning issues that arise due to different learning difficulties or the number of training samples available in each class. This overlook not only compromises the efficiency of the learning process but, more critically, the generalization capability of the resultant models. To address this problem, we introduce a novel adaptive strategy called KappaFace, which modulates the relative importance of each class based on its learning difficulty and imbalance. Drawing inspiration from the von Mises-Fisher distribution, KappaFace increases the margin values for the challenging or underrepresented classes and decreases that of more well-represented classes. Comprehensive experiments across eight cutting-edge baselines and nine well-established facial benchmark datasets strongly confirm the advantage of our method. Notably, we observed an enhancement of up to 0.5% on the verification task when evaluated on the IJB-B/C datasets. In conclusion, KappaFace offers a novel solution that effectively tackles imbalanced learning in deep face recognition tasks and establishes a new baseline.https://ieeexplore.ieee.org/document/10339282/Face recognitiondeep learningstatistical distributions |
| spellingShingle | Chingis Oinar Binh M. Le Simon S. Woo KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition IEEE Access Face recognition deep learning statistical distributions |
| title | KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition |
| title_full | KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition |
| title_fullStr | KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition |
| title_full_unstemmed | KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition |
| title_short | KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition |
| title_sort | kappaface adaptive additive angular margin loss for deep face recognition |
| topic | Face recognition deep learning statistical distributions |
| url | https://ieeexplore.ieee.org/document/10339282/ |
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