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

Severe weather conditions, such as rain and snow, adversely affect the visual quality of
images captured under such conditions, thus rendering them useless for further usage and …

The large domain discrepancy between faces captured in polarimetric (or conventional)
thermal and visible domains makes cross-domain face verification a highly challenging …

Near-infrared to visible (NIR-VIS) face recognition is the most common case in
heterogeneous face recognition, which aims to match a pair of face images captured from …

Thermal face imagery, which captures the naturally emitted heat from the face, is limited in
availability compared to face imagery in the visible spectrum. To help address this scarcity of …

Heterogeneous face recognition (HFR) aims at matching face images across different
domains. It is challenging due to the severe domain discrepancies and overfitting caused by …

Visible (VIS) to near infrared (NIR) face matching is a challenging problem due to the
significant domain discrepancy between the domains and a lack of sufficient data for training …

Recently, the hospital systems face a high influx of patients generated by several events,
such as seasonal flows or health crises related to epidemics (eg, COVID'19). Despite the …

This paper presents a novel approach, called Prototype-based Self-Distillation (ProS), for
unsupervised face representation learning. The existing supervised methods heavily rely on …

Abstract Convolutional Neural Networks (CNNs) are being increasingly used to address the
problem of iris presentation attack detection. In this work, we propose an explainable …