ArcFace
A loss function used in face recognition and metric learning. It enforces a fixed angular margin on feature embeddings in normalized angular space, imposing global inter-class separation. Compared with triplet loss, ArcFace provides stronger discriminative capacity for large-scale recognition tasks.
Core Facts
Timeline (last 90 days)
The V2 approach fine-tunes the backbone network for font identity using an ArcFace head, with approximately 35,000 font families as classes and family-level labels.
ArcFace first applies L2 normalization to both the feature embedding vectors and the classification weight matrix, then adds a fixed angular margin (typically 0.5 radians) to the cosine of the angle, scales the result, and feeds it into Softmax to compute the loss.
The core idea of loss functions such as ArcFace and CosFace is to artificially enlarge inter-class margins and compress intra-class distributions in angular space to improve discriminability.
ArcFace proposed the Additive Angular Margin Loss in 2018, achieving over 99.8% recognition accuracy on standard benchmarks like LFW
Popular face feature extraction models include FaceNet, ArcFace, and Dlib's ResNet model
All Facts (6)
ArcFace is a deep metric learning method optimized specifically for face recognition that outperforms general visual encoders in face identity feature extraction accuracy by introducing an additive angular margin loss function.
75%UnverifiedArcFace proposed the Additive Angular Margin Loss in 2018, achieving over 99.8% recognition accuracy on standard benchmarks like LFW
85%UnverifiedPopular face feature extraction models include FaceNet, ArcFace, and Dlib's ResNet model
85%UnverifiedThe V2 approach fine-tunes the backbone network for font identity using an ArcFace head, with approximately 35,000 font families as classes and family-level labels.
50%UnverifiedArcFace first applies L2 normalization to both the feature embedding vectors and the classification weight matrix, then adds a fixed angular margin (typically 0.5 radians) to the cosine of the angle, scales the result, and feeds it into Softmax to compute the loss.
50%UnverifiedThe core idea of loss functions such as ArcFace and CosFace is to artificially enlarge inter-class margins and compress intra-class distributions in angular space to improve discriminability.
50%