Over the last years deep learning methods have been shown to outperform previous state‐of‐
the‐art machine learning techniques in several fields, with computer vision being one of the …

Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

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 modern face recognition, the conventional pipeline consists of four stages: detect=>
align=> represent=> classify. We revisit both the alignment step and the representation step …

The key challenge of face recognition is to develop effective feature representations for
reducing intra-personal variations while enlarging inter-personal differences. In this paper …

The state-of-the-art of face recognition has been significantly advanced by the emergence of
deep learning. Very deep neural networks recently achieved great success on general …

This paper proposes to learn a set of high-level feature representations through deep
learning, referred to as Deep hidden IDentity features (DeepID), for face verification. We …

This paper designs a high-performance deep convolutional network (DeepID2+) for face
recognition. It is learned with the identification-verification supervisory signal. By increasing …

Abstract In 2007, Labeled Faces in the Wild was released in an effort to spur research in
face recognition, specifically for the problem of face verification with unconstrained images …

In the recent past, deep learning methods have demonstrated remarkable success for
supervised learning tasks in multiple domains including computer vision, natural language …