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Kostas Marias

Researcher at Foundation for Research & Technology – Hellas

Publications -  239
Citations -  3294

Kostas Marias is an academic researcher from Foundation for Research & Technology – Hellas. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 27, co-authored 212 publications receiving 2401 citations. Previous affiliations of Kostas Marias include American Hotel & Lodging Educational Institute & Mediterranean University.

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Stress and anxiety detection using facial cues from videos

TL;DR: Specific facial cues, derived from eye activity, mouth activity, head movements and camera based heart activity achieve good accuracy and are suitable as discriminative indicators of stress and anxiety.
Proceedings ArticleDOI

A novel method to detect Heart Beat Rate using a mobile phone

TL;DR: This paper proposes a system capable of estimating the heart beat rate using just a camera from a commercially available mobile phone, that the user does not need specialized hardware and can take a measurement in virtually any place under almost any circumstances.
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Automatic Assessment of Depression Based on Visual Cues: A Systematic Review

TL;DR: The review outlines methods and algorithms for visual feature extraction, dimensionality reduction, decision methods for classification and regression approaches, as well as different fusion strategies, for automatic depression assessment utilizing visual cues alone or in combination with vocal or verbal cues.
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Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays

TL;DR: This study presents an interpretable AI framework assessed by expert radiologists on the basis on how well the attention maps focus on the diagnostically-relevant image regions, achieving an overall area under the curve of 1 for a binary classification problem across a 5-fold training/testing dataset.
Proceedings ArticleDOI

Depression Assessment by Fusing High and Low Level Features from Audio, Video, and Text

TL;DR: The present paper reports on the results of the participants' participation to the depression sub-challenge of the sixth Audio/Visual Emotion Challenge (AVEC 2016), which was designed to compare feature modalities in gender-based and gender-independent modes using a variety of classification algorithms.