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 …

Face recognition has achieved great success in the last five years due to the development of
deep learning methods. However, deep convolutional neural networks (DCNNs) have been …

Deep face recognition has achieved great success due to large-scale training databases
and rapidly develo** loss functions. The existing algorithms devote to realizing an ideal …

Recently there has been a growing interest in Transformer not only in NLP but also in
computer vision. We wonder if transformer can be used in face recognition and whether it is …

Real-world face recognition applications often deal with suboptimal image quality or
resolution due to different capturing conditions such as various subject-to-camera distances …

In the field of face recognition, large-scale web-collected datasets are essential for learning
discriminative representations, but they suffer from noisy identity labels, such as outliers and …

This paper reviews the state of the art in visual privacy protection techniques, with particular
attention paid to techniques applicable to the field of Active and Assisted Living (AAL). A …

Abstract Knowledge distillation is an effective tool to compress large pre-trained
Convolutional Neural Networks (CNNs) or their ensembles into models applicable to mobile …