RAPS: Robust and efficient automatic construction of person-specific deformable models

Conference paper


Sagonas, C., Panagakis, Y., Zafeiriou, S. and Pantic, M. 2014. RAPS: Robust and efficient automatic construction of person-specific deformable models. 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA 23 - 28 Jun 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 1789-1796 https://doi.org/10.1109/CVPR.2014.231
TypeConference paper
TitleRAPS: Robust and efficient automatic construction of person-specific deformable models
AuthorsSagonas, C., Panagakis, Y., Zafeiriou, S. and Pantic, M.
Abstract

The construction of Facial Deformable Models (FDMs) is a very challenging computer vision problem, since the face is a highly deformable object and its appearance drastically changes under different poses, expressions, and illuminations. Although several methods for generic FDMs construction, have been proposed for facial landmark localization in still images, they are insufficient for tasks such as facial behaviour analysis and facial motion capture where perfect landmark localization is required. In this case, person-specific FDMs (PSMs) are mainly employed, requiring manual facial landmark annotation for each person and person-specific training. In this paper, a novel method for the automatic construction of PSMs is proposed. To this end, an orthonormal subspace which is suitable for facial image reconstruction is learnt. Next, to correct the fittings of a generic model, image congealing (i.e., batch image aliment) is performed by employing only the learnt orthonormal subspace. Finally, the corrected fittings are used to construct the PSM. The image congealing problem is solved by formulating a suitable sparsity regularized rank minimization problem. The proposed method outperforms the state-of-the art methods that is compared to, in terms of both landmark localization accuracy and computational time.

Conference2014 IEEE Conference on Computer Vision and Pattern Recognition
Page range1789-1796
ISSN1063-6919
ISBN
Hardcover9781479951185
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Publication dates
Print23 Jun 2014
Online25 Sep 2014
Publication process dates
Deposited06 Mar 2018
Accepted01 Jun 2014
Output statusPublished
Accepted author manuscript
Copyright Statement

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Digital Object Identifier (DOI)https://doi.org/10.1109/CVPR.2014.231
LanguageEnglish
Book title2014 IEEE Conference on Computer Vision and Pattern Recognition
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