dc.contributor.author | Goenetxea Imaz, Jon | |
dc.contributor.author | Unzueta Irurtia, Luis | |
dc.contributor.author | Elordi Hidalgo, Unai | |
dc.contributor.author | Otaegui Madurga, Oihana | |
dc.contributor.author | Dornaika, Fadi | |
dc.date.accessioned | 2021-09-02T10:09:07Z | |
dc.date.available | 2021-09-02T10:09:07Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) 5 : 680-687 (2021) | es_ES |
dc.identifier.isbn | 978-989-758-488-6 | |
dc.identifier.issn | 2184-4321 | |
dc.identifier.uri | http://hdl.handle.net/10810/52899 | |
dc.description.abstract | [EN] The communication between persons includes several channels to exchange information between individuals.
The non-verbal communication contains valuable information about the context of the conversation and it is
a key element to understand the entire interaction. The facial expressions are a representative example of this
kind of non-verbal communication and a valuable element to improve human-machine interaction interfaces.
Using images captured by a monocular camera, automatic facial analysis systems can extract facial expressions
to improve human-machine interactions. However, there are several technical factors to consider, including
possible computational limitations (e.g. autonomous robots), or data throughput (e.g. centralized computation
server). Considering the possible limitations, this work presents an efficient method to detect a set of 68
facial feature points and a set of key facial gestures at the same time. The output of this method includes
valuable information to understand the context of communication and improve the response of automatic
human-machine interaction systems. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | SciTePress, Science and Technology Publications, Lda | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | facial feature point detection | es_ES |
dc.subject | gesture recognition | es_ES |
dc.subject | multi-task learning | es_ES |
dc.title | Efficient multi-task based facial landmark and gesture detection in monocular images | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.holder | © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. CC BY-NC-ND 4.0 | es_ES |
dc.rights.holder | Atribución-NoComercial-SinDerivadas 3.0 España | * |
dc.relation.publisherversion | https://www.scitepress.org/Link.aspx?doi=10.5220/0010373006800687 | es_ES |
dc.identifier.doi | 10.5220/0010373006800687 | |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | es_ES |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala | es_ES |