연구 및 출판물
ArcFace
Additive Angular Margin Loss for Deep Face Recognition
ArcFace introduced a simple but highly effective angular margin objective that made face embeddings more discriminative at production scale.
논문 정보
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
게재 정보
CVPR 2019
저자
Jiankang Deng, Jia Guo, Niannan Xue, Stefanos Zafeiriou
연구 개요
ArcFace became one of the most influential face recognition papers because it improves how identity classes are separated in embedding space while keeping training practical. The method is widely used as a strong baseline for verification, identification, search, and account security pipelines.
프로덕션 활용 사례
- Identity verification and digital onboarding
- Access control and workforce authentication
- Duplicate account detection and fraud reduction
- Large-scale face search and watchlist matching
핵심 기여
Adds an explicit angular margin so the model learns tighter same-person clusters and clearer separation between identities.
Improved benchmark performance on major face recognition evaluations helped establish ArcFace as a standard loss for modern face embeddings.
Works naturally with large-scale recognition systems that need stable similarity scores for matching, deduplication, and watchlist search.
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