Deep face recognition has achieved remarkable improvements due to the introduction of
margin-based softmax loss, in which the prototype stored in the last linear layer represents …

Recognizing wild faces is extremely hard as they appear with all kinds of variations.
Traditional methods either train with specifically annotated variation data from target …

As an emerging topic in face recognition, designing margin-based loss functions can
increase the feature margin between different classes for enhanced discriminability. More …

State-of-the-art deep face recognition methods are mostly trained with a softmax-based multi-
class classification framework. Despite being popular and effective, these methods still have …

Learning discriminative deep feature embeddings by using million-scale in-the-wild datasets
and margin-based softmax loss is the current state-of-the-art approach for face recognition …

The cosine-based softmax losses and their variants achieve great success in deep learning
based face recognition. However, hyperparameter settings in these losses have significant …

Recently, large-margin softmax loss methods, such as angular softmax loss (SphereFace),
large margin cosine loss (CosFace), and additive angular margin loss (ArcFace), have …

Training large-scale unbalanced data is the central topic in face recognition. In the past two
years, face recognition has achieved remarkable improvements due to the introduction of …

Learning discriminative face features plays a major role in building high-performing face
recognition models. The recent state-of-the-art face recognition solutions proposed to …

Recent advances in deep learning have significantly increased the performance of face
recognition systems. The performance and reliability of these models depend heavily on the …