A large number of intelligent models for masked face recognition (MFR) has been recently
presented and applied in various fields, such as masked face tracking for people safety or …

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

This paper introduces a powerful channel augmented joint learning strategy for the visible-
infrared recognition problem. For data augmentation, most existing methods directly adopt …

Conventional multiview clustering seeks to partition data into respective groups based on
the assumption that all views are fully observed. However, in practical applications, such as …

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

Heterogeneous face recognition (HFR) aims at matching facial images acquired from
different sensing modalities with mission-critical applications in forensics, security and …

In this paper, we propose a general framework for incomplete multiview clustering. The
proposed method is the first work that exploits the graph learning and spectral clustering …

Multi-view clustering aims to partition data collected from diverse sources based on the
assumption that all views are complete. However, such prior assumption is hardly satisfied …

Nowadays multi-modal visual data are much easier to access as the technology develops.
Nevertheless, there is an underlying problem hidden behind the emerging multi-modality …

Near infrared-visible (NIR-VIS) heterogeneous face recognition refers to the process of
matching NIR to VIS face images. Current heterogeneous methods try to extend VIS face …