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 …

It is well known that deep learning approaches to face recognition suffer from various biases
in the available training data. In this work, we demonstrate the large potential of synthetic …

With the recent success of deep neural networks, remarkable progress has been achieved
on face recognition. However, collecting large-scale real-world training data for face …

State-of-the-art face recognition models show impressive accuracy, achieving over 99.8%
on Labeled Faces in the Wild (LFW) dataset. Such models are trained on large-scale …

Over the past years, the main research innovations in face recognition focused on training
deep neural networks on large-scale identity-labeled datasets using variations of multi-class …

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 …

State-of-the-art face recognition networks are often computationally expensive and cannot
be used for mobile applications. Training lightweight face recognition models also requires …

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 …

Cosine-based softmax losses significantly improve the performance of deep face recognition
networks. However, these losses always include sensitive hyper-parameters which can …

Abstract Face representation using Deep Convolutional Neural Network (DCNN)
embedding is the method of choice for face recognition. Current state-of-the-art face …