no code implementations • 20 Jan 2018 • Niannan Xue, Jiankang Deng, Shiyang Cheng, Yannis Panagakis, Stefanos Zafeiriou
Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of various visual data.
no code implementations • CVPR 2018 • Jiankang Deng, Shiyang Cheng, Niannan Xue, Yuxiang Zhou, Stefanos Zafeiriou
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.
no code implementations • 14 Sep 2017 • Niannan Xue, Jiankang Deng, Yannis Panagakis, Stefanos Zafeiriou
We revisit the problem of robust principal component analysis with features acting as prior side information.
no code implementations • ICCV 2017 • Niannan Xue, Yannis Panagakis, Stefanos Zafeiriou
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
100 code implementations • CVPR 2019 • Jiankang Deng, Jia Guo, Jing Yang, Niannan Xue, Irene Kotsia, Stefanos Zafeiriou
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)
3 code implementations • 5 Dec 2018 • Jia Guo, Jiankang Deng, Niannan Xue, Stefanos Zafeiriou
Face Analysis Project on MXNet
Ranked #1 on Face Alignment on IBUG