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StillNoFace SIGNED

Identity matching from still images without face information

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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Project "StillNoFace" data sheet

The following table provides information about the project.

Coordinator
PANEPISTIMIO IOANNINON 

Organization address
address: PANEPISTEMIOYPOLE PANEPISTEMIO IOANNINON
city: IOANNINA
postcode: 45110
website: www.uoi.gr / www.rc.uoi.gr

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Greece [EL]
 Project website http://www.cs.uoi.gr/
 Total cost 168˙391 €
 EC max contribution 168˙391 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2014
 Funding Scheme MSCA-IF-GF
 Starting year 2015
 Duration (year-month-day) from 2015-09-01   to  2017-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    PANEPISTIMIO IOANNINON EL (IOANNINA) coordinator 168˙391.00
2    University of Houston System US (HOUSTON) partner 0.00

Map

 Project objective

In computer vision, human identity matching from images and/or video has been an active research topic for more than two decades and its popularity is increasing with the increase in computing power. The state of the art techniques are based on face images and gait recognition from long video sequences. However, in many real applications only some static images of the subject may be available where face information is missing (e.g. posterior views). These scenarios have not been addressed by the research community as they are difficult to handle. In this action, we propose a method for matching identities from a set of 2D images of a person without any facial information. The method consists of two steps: at first, the human body is modelled by a 3D articulated model whose pose is estimated by its 2D projections onto the images. Then, biometric features are computed by fitting 3D deformable models to the image data, thus capturing the form and size of the main parts of the anatomy. The overall framework works under a probabilistic framework, with a learning step, in order to encode pose and anatomy variations between a set of individuals that are to be identified.

 Publications

year authors and title journal last update
List of publications.
2017 G. Sfikas, B. Gatos and C. Nikou.
SemiCCA: a new supervised probabilistic CCA model for keyword spotting.
published pages: 1107-1111, ISSN: , DOI:
IEEE International Conference on Image Processing (ICIP’18) 17-20 September 2017, Beijing, 2019-06-13
2017 Michalis Vrigkas, Evangelos Kazakos, Christophoros Nikou, Ioannis A. Kakadiaris
Inferring Human Activities Using Robust Privileged Probabilistic Learning.
published pages: , ISSN: , DOI:
4th Workshop on Transferring and Adapting Source Knowledge in Computer Vision 22-29 October 2017, Venice, Ita 2019-06-13
2017 Nikolaos Sarafianos, Theodore Giannakopoulos, Christophoros Nikou, Ioannis A. Kakadiaris
Curriculum Learning for Multi-Task Classification of Visual Attributes.
published pages: , ISSN: , DOI:
4th Workshop on Transferring and Adapting Source Knowledge in Computer Vision 22-29 October 2017, Venice, Ita 2019-06-13
2017 C. Nikou
MAP tomographic reconstruction with a spatially adaptive hierarchical image model.
published pages: 1594-1598, ISSN: , DOI:
25th European Signal Processing Conference (EUSIPCO\'17) 28 August-2 September 2017, Kos 2019-06-13

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The information about "STILLNOFACE" are provided by the European Opendata Portal: CORDIS opendata.

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