Please use this identifier to cite or link to this item:

TitleEfficient generic face model fitting to images and videos
Author(s)Unzueta, Luis
Pimenta, Waldir
Goenetxea, Jon
Santos, Luís Paulo
Dornaika, Fadi
KeywordsFace model fitting
Face tracking
Head pose estimation
Facial feature detection
Face model fitting
Issue dateMay-2014
JournalImage and Vision Computing
Abstract(s)In this paper we present a robust and lightweight method for the automatic fitting of deformable 3D face models on facial images. Popular fitting techniques such as those based on statistical models of shape and appearance require a training stage based on a set of facial images and their corresponding facial landmarks, which have to be manually labeled. Therefore, new images in which to fit the model cannot differ too much in shape and appearance (including illumination variation, facial hair, wrinkles, etc.) from those used for training. By contrast, our approach can fit a generic face model in two steps: (1) the detection of facial features based on local image gradient analysis and (2) the backprojection of a deformable 3D face model through the optimization of its deformation parameters. The proposed approach can retain the advantages of both learning-free and learning-based approaches. Thus, we can estimate the position, orientation, shape and actions of faces, and initialize user-specific face tracking approaches, such as Online Appearance Models (OAMs), which have shown to be more robust than generic user tracking approaches. Experimental results show that our method outperforms other fitting alternatives under challenging illumination conditions and with a computational cost that allows its implementation in devices with low hardware specifications, such as smartphones and tablets. Our proposed approach lends itself nicely to many frameworks addressing semantic inference in face images and videos.
AccessOpen access
Appears in Collections:DI/CCTC - Artigos (papers)

Files in This Item:
File Description SizeFormat 
IMAVIS2014-FaceRecognition.pdfDocumento principal938,51 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID