S
Stefanos Zafeiriou
Researcher at Imperial College London
Publications - 406
Citations - 26443
Stefanos Zafeiriou is an academic researcher from Imperial College London. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 60, co-authored 375 publications receiving 17993 citations. Previous affiliations of Stefanos Zafeiriou include Huawei & Aristotle University of Thessaloniki.
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Analysing Affective Behavior in the First ABAW 2020 Competition.
TL;DR: This paper describes the Affective Behavior Analysis in-the-wild 2020 Competition, the first Competition aiming at automatic analysis of the three main behavior tasks of valencearousal estimation, basic expression recognition and action unit detection, and presents the evaluation metrics.
Proceedings Article
Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces.
TL;DR: This paper relaxes the intra-class constraint of ArcFace to improve the robustness to label noise and designs K sub-centers for each class and the training sample only needs to be close to any of the K positive subcenters instead of the only one positive center.
Journal ArticleDOI
ArcFace: Additive Angular Margin Loss for Deep Face Recognition.
TL;DR: Zhang et al. as mentioned in this paper proposed an additive angular margin loss (ArcFace), which not only has a clear geometric interpretation, but also significantly enhances the discriminative power.
Proceedings ArticleDOI
Recognition of Affect in the Wild Using Deep Neural Networks
TL;DR: This paper utilizes the first large-scale "in-the-wild" (Aff-Wild) database, which is annotated in terms of the valence-arousal dimensions, to train and test an end-to-end deep neural architecture for the estimation of continuous emotion dimensions based on visual cues.
Proceedings ArticleDOI
3D Face Morphable Models "In-the-Wild"
James Booth,Epameinondas Antonakos,Stylianos Ploumpis,George Trigeorgis,Yannis Panagakis,Stefanos Zafeiriou +5 more
TL;DR: This paper proposes the first, to the best of the knowledge, in-the-wild 3DMM by combining a powerful statistical model of facial shape, which describes both identity and expression, with an in- the-wild texture model, and demonstrates the first 3D facial database with relatively unconstrained conditions.