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Irene Kotsia
Researcher at Middlesex University
Publications - 61
Citations - 5327
Irene Kotsia is an academic researcher from Middlesex University. The author has contributed to research in topics: Facial expression & Facial recognition system. The author has an hindex of 24, co-authored 61 publications receiving 3405 citations. Previous affiliations of Irene Kotsia include Aristotle University of Thessaloniki & University of London.
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Journal ArticleDOI
Tensor Learning for Regression
TL;DR: Experiments conducted for the problems of head-pose, human-age, and 3-D body-pose estimations using real data from publicly available databases verified not only the superiority of tensors over their vector counterparts but also the efficiency of the proposed algorithms.
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Texture and shape information fusion for facial expression and facial action unit recognition
TL;DR: A novel method based on fusion of texture and shape information is proposed for facial expression and Facial Action Unit (FAU) recognition from video sequences and the accuracy achieved is 92.3% when recognizing the seven basic facial expressions.
Journal ArticleDOI
A Novel Discriminant Non-Negative Matrix Factorization Algorithm With Applications to Facial Image Characterization Problems
TL;DR: A novel DNMF method that uses projected gradients is presented that employs some extra modifications that make the method more suitable for classification tasks.
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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
4DFAB: A Large Scale 4D Database for Facial Expression Analysis and Biometric Applications
TL;DR: 4DFAB is a new large scale database of dynamic high-resolution 3D faces (over 1,800,000 3D meshes) that can be used for both face and facial expression recognition, as well as behavioural biometrics.