Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Drira, Hassen"'
Publikováno v:
IEEE International Workshop on MultiMedia Signal Processing (MMSP 2023)
The Transformer architecture has gained significant popularity in computer vision tasks due to its capacity to generalize and capture long-range dependencies. This characteristic makes it well-suited for generating spatiotemporal tokens from videos.
Externí odkaz:
http://arxiv.org/abs/2310.14416
In this paper, an effective pipeline to automatic 4D Facial Expression Recognition (4D FER) is proposed. It combines two growing but disparate ideas in Computer Vision -- computing the spatial facial deformations using tools from Riemannian geometry
Externí odkaz:
http://arxiv.org/abs/2105.02319
Deep Learning architectures, albeit successful in most computer vision tasks, were designed for data with an underlying Euclidean structure, which is not usually fulfilled since pre-processed data may lie on a non-linear space. In this paper, we prop
Externí odkaz:
http://arxiv.org/abs/2011.12004
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence 2019
The detection and tracking of human landmarks in video streams has gained in reliability partly due to the availability of affordable RGB-D sensors. The analysis of such time-varying geometric data is playing an important role in the automatic human
Externí odkaz:
http://arxiv.org/abs/1908.03231
Autor:
Drira, Hassen
La reconnaissance de visage automatique offre de nombreux avantages par rapport aux autres technologies biométriques en raison de la nature non-intrusive. Ainsi, les techniques de reconnaissance faciale ont reçu une attention croissante au sein de
Externí odkaz:
http://www.theses.fr/2011LIL10075/document
Autor:
Drira, Hassen
Dans cette thèse, nous proposons un cadre Riemannien pour comparer, déformer, calculer des statistiques et organiser de manière hiérarchique des surfaces faciales. Nous appliquons ce cadre à la biométrie faciale 3D où les défis sont les expre
Externí odkaz:
http://tel.archives-ouvertes.fr/tel-00728009
http://tel.archives-ouvertes.fr/docs/00/72/80/09/PDF/Dissertation_DRIRA.pdf
http://tel.archives-ouvertes.fr/docs/00/72/80/09/PDF/Dissertation_DRIRA.pdf
This paper describes a novel framework for computing geodesic paths in shape spaces of spherical surfaces under an elastic Riemannian metric. The novelty lies in defining this Riemannian metric directly on the quotient (shape) space, rather than inhe
Externí odkaz:
http://arxiv.org/abs/1506.03065
Akademický článek
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Publikováno v:
In Image and Vision Computing February 2018 70:32-45
Autor:
Kurtek, Sebastian, Drira, Hassen
Publikováno v:
In Computers & Graphics October 2015 51:52-59