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

Sagonas, Christos, Panagakis, Yannis ORCID logoORCID: https://orcid.org/0000-0003-0153-5210, Zafeiriou, Stefanos and Pantic, Maja (2014) RAPS: Robust and efficient automatic construction of person-specific deformable models. 2014 IEEE Conference on Computer Vision and Pattern Recognition. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, 23-28 June 2014, Columbus, OH, USA. ISBN 9781479951185. ISSN 1063-6919 [Conference or Workshop Item] (doi:10.1109/CVPR.2014.231)

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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.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 23773
Notes on copyright: © 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.
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Depositing User: Yannis Panagakis
Date Deposited: 06 Mar 2018 15:15
Last Modified: 29 Nov 2022 23:30
URI: https://eprints.mdx.ac.uk/id/eprint/23773

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