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 …

Learning discriminative deep feature embeddings by using million-scale in-the-wild datasets
and margin-based softmax loss is the current state-of-the-art approach for face recognition …

Training large-scale unbalanced data is the central topic in face recognition. In the past two
years, face recognition has achieved remarkable improvements due to the introduction of …

Face recognition systems are usually faced with unseen domains in real-world applications
and show unsatisfactory performance due to their poor generalization. For example, a well …

With the advent of the era of big data in the world and the commercial value of face
recognition technology, the prospects for face recognition technology are very bright and …

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 …

Face recognition has achieved advanced development by using convolutional neural
network (CNN) based recognizers. Existing recognizers typically demonstrate powerful …

Long-tailed problem has been an important topic in face recognition task. However, existing
methods only concentrate on the long-tailed distribution of classes. Differently, we devote to …

Cross-resolution face recognition (CRFR), which is important in intelligent surveillance and
biometric forensics, refers to the problem of matching a low-resolution (LR) probe face …

Currently, it is ever more common to access online services for activities which formerly
required physical attendance. From banking operations to visa applications, a significant …