Robust Face Recognition
19 papers with code • 0 benchmarks • 4 datasets
Robust face recognition is the task of performing recognition in an unconstrained environment, where there is variation of view-point, scale, pose, illumination and expression of the face images.
( Image credit: MeGlass dataset )
Benchmarks
These leaderboards are used to track progress in Robust Face Recognition
Most implemented papers
Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces.
Face Synthesis for Eyeglass-Robust Face Recognition
A feasible method is to collect large-scale face images with eyeglasses for training deep learning methods.
Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network
Deep Convolutional Neural Networks (CNNs) have been pushing the frontier of the face recognition research in the past years.
Occlusion Robust Face Recognition Based on Mask Learning With Pairwise Differential Siamese Network
Inspired by the fact that human visual system explicitly ignores the occlusion and only focuses on the non-occluded facial areas, we propose a mask learning strategy to find and discard corrupted feature elements from recognition.
Face Recognition via Locality Constrained Low Rank Representation and Dictionary Learning
First, a low-rank representation is introduced to handle the possible contamination of the training as well as test data.
Masked Face Recognition with Latent Part Detection
The proposed LPD model is trained in an end-to-end manner and only utilizes the original and synthetic training data.
OSTeC: One-Shot Texture Completion
Many recent 3D facial texture reconstruction and pose manipulation from a single image approaches still rely on large and clean face datasets to train image-to-image Generative Adversarial Networks (GANs).
LARNet: Lie Algebra Residual Network for Face Recognition
We prove that face rotation in the image space is equivalent to an additive residual component in the feature space of CNNs, which is determined solely by the rotation.
Embedding Non-Distortive Cancelable Face Template Generation
Biometric authentication systems are crucial for security, but developing them involves various complexities, including privacy, security, and achieving high accuracy without directly storing pure biometric data in storage.