A large number of intelligent models for masked face recognition (MFR) has been recently
presented and applied in various fields, such as masked face tracking for people safety or …

The limited capacity to recognise faces under occlusions is a long‐standing problem that
presents a unique challenge for face recognition systems and even humans. The problem …

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 …

Face recognition techniques have been developed significantly in recent years. However,
recognizing faces with partial occlusion is still challenging for existing face recognizers …

Due to the difficulty in acquiring massive task-specific occluded images, the classification of
occluded images with deep convolutional neural networks (CNNs) remains highly …

Deep CNNs have been pushing the frontier of visual recognition over past years. Besides
recognition accuracy, strong demands in understanding deep CNNs in the research …

Despite the recent success of convolutional neural networks for computer vision
applications, unconstrained face recognition remains a challenge. In this work, we make two …

Face occlusion has been a long standing challenging issue in face recognition. In the state-
of-the-art deep Convolutional Neural Network (CNN) face recognition models, occluded …

In recent decades, 3D morphable model (3DMM) has been commonly used in image-based
photorealistic 3D face reconstruction. However, face images are often corrupted by serious …

Few-shot face recognition under occlusion (FSFRO) aims to recognize novel subjects given
only a few, probably occluded face images, and it is challenging and common in real-world …