Heterogeneous face recognition (HFR) refers to matching face imagery across different
domains. It has received much interest from the research community as a result of its …

The volume of convolutional neural network (CNN) models proposed for face recognition
has been continuously growing larger to better fit the large amount of training data. When …

Ocular biometrics encompasses the imaging and use of characteristic features extracted
from the eyes for personal recognition. Ocular biometric modalities in visible light have …

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

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 …

Heterogeneous face recognition (HFR) refers to matching cross-domain faces and plays a
crucial role in public security. Nevertheless, HFR is confronted with challenges from large …

In this paper, we propose a simultaneous local binary feature learning and encoding
(SLBFLE) approach for both homogeneous and heterogeneous face recognition. Unlike …

Visual versus near infrared (VIS-NIR) face recognition is still a challenging heterogeneous
task due to large appearance difference between VIS and NIR modalities. This paper …

RGB-Infrared (RGB-IR) cross-modality person re-identification (re-ID) is attracting more and
more attention due to requirements for 24-h scene surveillance. However, the high cost of …

Surveillance cameras today often capture NIR (near infrared) images in low-light
environments. However, most face datasets accessible for training and verification are only …