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.
Papers
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Journal ArticleDOI
300 Faces In-The-Wild Challenge
Christos Sagonas,Epameinondas Antonakos,Georgios Tzimiropoulos,Stefanos Zafeiriou,Maja Pantic +4 more
TL;DR: This paper proposes a semi-automatic annotation technique that was employed to re-annotate most existing facial databases under a unified protocol, and presents the 300 Faces In-The-Wild Challenge (300-W), the first facial landmark localization challenge that was organized twice, in 2013 and 2015.
Proceedings ArticleDOI
Robust Discriminative Response Map Fitting with Constrained Local Models
TL;DR: A novel discriminative regression based approach for the Constrained Local Models (CLMs) framework, referred to as the Discriminative Response Map Fitting (DRMF) method, which shows impressive performance in the generic face fitting scenario.
Proceedings ArticleDOI
AgeDB: The First Manually Collected, In-the-Wild Age Database
Stylianos Moschoglou,Athanasios Papaioannou,Christos Sagonas,Jiankang Deng,Irene Kotsia,Stefanos Zafeiriou +5 more
TL;DR: This paper presents the first, to the best of knowledge, manually collected "in-the-wild" age database, dubbed AgeDB, containing images annotated with accurate to the year, noise-free labels, which renders AgeDB suitable when performing experiments on age-invariant face verification, age estimation and face age progression "in the wild".
Journal ArticleDOI
End-to-End Multimodal Emotion Recognition Using Deep Neural Networks
TL;DR: This work proposes an emotion recognition system using auditory and visual modalities using a convolutional neural network to extract features from the speech, while for the visual modality a deep residual network of 50 layers is used.
Proceedings ArticleDOI
Incremental Face Alignment in the Wild
TL;DR: It is shown that it is possible to automatically construct robust discriminative person and imaging condition specific models 'in- the-wild' that outperform state-of-the-art generic face alignment strategies.