Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

Deep learning models currently achieve human levels of performance on real-world face
recognition tasks. We review scientific progress in understanding human face processing …

Deep learning, in particular the deep convolutional neural networks, has received
increasing interests in face recognition recently, and a number of deep learning methods …

In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset
contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each …

Despite the large volume of face recognition datasets, there is a significant portion of
subjects, of which the samples are insufficient and thus under-represented. Ignoring such …

In recent years, the performance of face verification systems has significantly improved using
deep convolutional neural networks (DCNNs). A typical pipeline for face verification includes …

The volume of convolutional neural network (CNN) models proposed for face recognition
has been continuously growing larger to better fit the large amount of training data. When …

We present a multi-purpose algorithm for simultaneous face detection, face alignment, pose
estimation, gender recognition, smile detection, age estimation and face recognition using a …

This paper presents a Neural Aggregation Network (NAN) for video face recognition. The
network takes a face video or face image set of a person with a variable number of face …

The availability of large annotated datasets and affordable computation power have led to
impressive improvements in the performance of convolutional neural networks (CNNs) on …