G
Grigorios Chrysos
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 78
Citations - 986
Grigorios Chrysos is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Computer science & Software. The author has an hindex of 11, co-authored 64 publications receiving 684 citations. Previous affiliations of Grigorios Chrysos include Technical University of Crete & University of Crete.
Papers
More filters
Proceedings ArticleDOI
The Menpo Facial Landmark Localisation Challenge: A Step Towards the Solution
TL;DR: A new benchmark for facial landmark localisation, contrary to the previous benchmarks, contains facial images both in (nearly) frontal, as well as in profile pose (annotated with a different markup of facial landmarks).
Journal ArticleDOI
A Comprehensive Performance Evaluation of Deformable Face Tracking In-the-Wild
TL;DR: This paper performs the first, to the best of the knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300 VW benchmark and reveals future avenues for further research on the topic.
Journal ArticleDOI
The Menpo Benchmark for Multi-pose 2D and 3D Facial Landmark Localisation and Tracking
Jiankang Deng,Anastasios Roussos,Grigorios Chrysos,Evangelos Ververas,Irene Kotsia,Jie Shen,Stefanos Zafeiriou,Stefanos Zafeiriou +7 more
TL;DR: An elaborate semi-automatic methodology is introduced for providing high-quality annotations for both the Menpo 2D and Menpo 3D benchmarks, two new datasets for multi-pose 2d and 3D facial landmark localisation and tracking.
Proceedings ArticleDOI
The 3D Menpo Facial Landmark Tracking Challenge
Stefanos Zafeiriou,Grigorios Chrysos,Anastasios Roussos,Evangelos Ververas,Jiankang Deng,George Trigeorgis +5 more
TL;DR: The efforts to develop a very large database suitable to be used to train 3D face alignment algorithms in images captured "in-the-wild" and to train and evaluate new methods for3D face landmark tracking are summarized.
Journal ArticleDOI
Tensor Methods in Computer Vision and Deep Learning
Yannis Panagakis,Jean Kossaifi,Grigorios Chrysos,James Oldfield,Mihalis A. Nicolaou,Animashree Anandkumar,Stefanos Zafeiriou +6 more
TL;DR: This article provides an in-depth and practical review of tensors and tensor methods in the context of representation learning and deep learning, with a particular focus on visual data analysis and computer vision applications.