The capacity to recognize faces under varied poses is a fundamental human ability that
presents a unique challenge for computer vision systems. Compared to frontal face …

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 …

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 …

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 …

Cross-modality face recognition is an emerging topic due to the wide-spread usage of
different sensors in day-to-day life applications. The development of face recognition …

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 …

Face recognition is an efficient technique and one of the most preferred biometric modalities
for the identification and verification of individuals as compared to voice, fingerprint, iris …

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 …

Deep convolutional neural networks have recently proven extremely effective for difficult
face recognition problems in uncontrolled settings. To train such networks, very large …