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

Face recognition (FR) is one of the most popular and long-standing topics in computer
vision. With the recent development of deep learning techniques and large-scale datasets …

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 the field of face recognition, a model learns to distinguish millions of face images with
fewer dimensional embedding features, and such vast information may not be properly …

In recent years, researchers have proposed many deep learning (DL) methods for various
tasks, and particularly face recognition (FR) made an enormous leap using these …

In this paper, we propose a new supervision objective named uniform loss to learn deep
equidistributed representations for face recognition. Most existing methods aim to learn …

Deep learning has achieved a great success in face recognition (FR), however, few existing
models take hierarchical multi-scale local features into consideration. In this work, we …

Recent years witnessed the breakthrough of face recognition with deep convolutional neural
networks. Dozens of papers in the field of FR are published every year. Some of them were …

Face anti-spoofing is an important task to assure the security of face recognition systems. To
be applicable to unconstrained real-world environments, generalization capabilities of the …

The datasets of face recognition contain an enormous number of identities and instances.
However, conventional methods have difficulty in reflecting the entire distribution of the …