The success of deep learning has been a catalyst to solving increasingly complex machine-
learning problems, which often involve multiple data modalities. We review recent advances …

The performance of a biometric system that relies on a single biometric modality (eg,
fingerprints only) is often stymied by various factors such as poor data quality or limited …

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

Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …

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 …

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 …