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Found 6 papers, 2 papers with code
Side Information in Robust Principal Component Analysis: Algorithms and Applications
Robust Principal Component Analysis (RPCA) aims at recovering a low-rank subspace from grossly corrupted high-dimensional (often visual) data and is a cornerstone in many machine learning and computer vision applications.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Informed Non-convex Robust Principal Component Analysis with Features
We revisit the problem of robust principal component analysis with features acting as prior side information.
UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition
We demonstrate that by attaching the completed UV to the fitted mesh and generating instances of arbitrary poses, we can increase pose variations for training deep face recognition/verification models, and minimise pose discrepancy during testing, which lead to better performance.
Side Information for Face Completion: a Robust PCA Approach
Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of various visual data.
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability.
Ranked #1 on
Face Verification
on Labeled Faces in the Wild
(using extra training data)
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
Face Analysis Project on MXNet
Ranked #1 on
Face Alignment
on IBUG
