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 …

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 …

Person recognition at a distance entails recognizing the identity of an individual appearing
in images or videos collected by long-range imaging systems such as drones or surveillance …

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

Heterogeneous Face Recognition (HFR) refers to matching face images captured in different
domains, such as thermal to visible images (VIS), sketches to visible images, near-infrared …

Thermal-to-visible face verification is a challenging problem due to the large domain
discrepancy between the modalities. Existing approaches either attempt to synthesize …

Abstract Near-infrared-visible (NIR-VIS) heterogeneous face recognition matches NIR to
corresponding VIS face images. However, due to the sensing gap, NIR images often lose …

The thermal-to-visible (T2V) face translation task is essential for enabling face verification in
low-light or dark conditions by converting thermal infrared faces into their visible …

Modern-day surveillance systems perform person recognition using deep learning-based
face verification networks. Most state-of-the-art facial verification systems are trained using …