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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.
Papers
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Proceedings ArticleDOI
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
TL;DR: This paper presents arguably the most extensive experimental evaluation against all recent state-of-the-art face recognition methods on ten face recognition benchmarks, and shows that ArcFace consistently outperforms the state of the art and can be easily implemented with negligible computational overhead.
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ArcFace: Additive Angular Margin Loss for Deep Face Recognition
TL;DR: This article proposed an additive angular margin loss (ArcFace) to obtain highly discriminative features for face recognition, which has a clear geometric interpretation due to the exact correspondence to the geodesic distance on the hypersphere.
Proceedings ArticleDOI
300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge
TL;DR: The main goal of this challenge is to compare the performance of different methods on a new-collected dataset using the same evaluation protocol and the same mark-up and hence to develop the first standardized benchmark for facial landmark localization.
Proceedings ArticleDOI
Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network
George Trigeorgis,Fabien Ringeval,Raymond Brueckner,Erik Marchi,Mihalis A. Nicolaou,Björn Schuller,Stefanos Zafeiriou +6 more
TL;DR: This paper proposes a solution to the problem of `context-aware' emotional relevant feature extraction, by combining Convolutional Neural Networks (CNNs) with LSTM networks, in order to automatically learn the best representation of the speech signal directly from the raw time representation.
Proceedings ArticleDOI
RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild
TL;DR: A novel single-shot, multi-level face localisation method, named RetinaFace, which unifies face box prediction, 2D facial landmark localisation and 3D vertices regression under one common target: point regression on the image plane.