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Face Alignment with Part-Based Modeling
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-4181-2753
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2011 (English)In: BMVC 2011 - Proceedings of the British Machine Vision Conference 2011 / [ed] Hoey, Jesse and McKenna, Stephen and Trucco, Emanuele, UK: British Machine Vision Association, BMVA , 2011, 27.1-27.10 p.Conference paper (Refereed)
Abstract [en]

We propose a new method for face alignment with part-based modeling. This method is competitive in terms of precision with existing methods such as Active Appearance Models, but is more robust and has a superior generalization ability due to its part-based nature. A variation of the Histogram of Oriented Gradients descriptor is used to model the appearance of each part and the shape information is represented with a set of landmark points around the major facial features. Multiple linear regression models are learnt to estimate the position of the landmarks from the appearance of each part. We verify our algorithm with a set of experiments on human faces and these show the competitive performance of our method compared to existing methods.

Place, publisher, year, edition, pages
UK: British Machine Vision Association, BMVA , 2011. 27.1-27.10 p.
Keyword [en]
Linear regression, Active appearance models, Competitive performance, Facial feature, Generalization ability, Histogram of oriented gradients, Multiple linear regression models, Part-based models, Shape information
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:kth:diva-45452DOI: 10.5244/C.25.27ISI: 000346360200030ScopusID: 2-s2.0-84898413537ISBN: 1-901725-43-XOAI: diva2:452323
2011 22nd British Machine Vision Conference, BMVC 2011, Dundee, United Kingdom, 29 August 2011 through 2 September 2011

QC 20111116

Available from: 2011-10-28 Created: 2011-10-28 Last updated: 2015-04-22Bibliographically approved
In thesis
1. Correspondence Estimation in Human Face and Posture Images
Open this publication in new window or tab >>Correspondence Estimation in Human Face and Posture Images
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Many computer vision tasks such as object detection, pose estimation,and alignment are directly related to the estimation of correspondences overinstances of an object class. Other tasks such as image classification andverification if not completely solved can largely benefit from correspondenceestimation. This thesis presents practical approaches for tackling the corre-spondence estimation problem with an emphasis on deformable objects.Different methods presented in this thesis greatly vary in details but theyall use a combination of generative and discriminative modeling to estimatethe correspondences from input images in an efficient manner. While themethods described in this work are generic and can be applied to any object,two classes of objects of high importance namely human body and faces arethe subjects of our experimentations.When dealing with human body, we are mostly interested in estimating asparse set of landmarks – specifically we are interested in locating the bodyjoints. We use pictorial structures to model the articulation of the body partsgeneratively and learn efficient discriminative models to localize the parts inthe image. This is a common approach explored by many previous works. Wefurther extend this hybrid approach by introducing higher order terms to dealwith the double-counting problem and provide an algorithm for solving theresulting non-convex problem efficiently. In another work we explore the areaof multi-view pose estimation where we have multiple calibrated cameras andwe are interested in determining the pose of a person in 3D by aggregating2D information. This is done efficiently by discretizing the 3D search spaceand use the 3D pictorial structures model to perform the inference.In contrast to the human body, faces have a much more rigid structureand it is relatively easy to detect the major parts of the face such as eyes,nose and mouth, but performing dense correspondence estimation on facesunder various poses and lighting conditions is still challenging. In a first workwe deal with this variation by partitioning the face into multiple parts andlearning separate regressors for each part. In another work we take a fullydiscriminative approach and learn a global regressor from image to landmarksbut to deal with insufficiency of training data we augment it by a large numberof synthetic images. While we have shown great performance on the standardface datasets for performing correspondence estimation, in many scenariosthe RGB signal gets distorted as a result of poor lighting conditions andbecomes almost unusable. This problem is addressed in another work wherewe explore use of depth signal for dense correspondence estimation. Hereagain a hybrid generative/discriminative approach is used to perform accuratecorrespondence estimation in real-time.

Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2014. vii, 32 p.
TRITA-CSC-A, ISSN 1653-5723 ; 2014:14
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
urn:nbn:se:kth:diva-150115 (URN)978-91-7595-261-1 (ISBN)
Public defence
2014-10-10, Kollegiesalen, Brinellvägen 8, KTH, Stockholm, 10:00 (English)

QC 20140919

Available from: 2014-09-19 Created: 2014-08-29 Last updated: 2014-09-19Bibliographically approved

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Publisher's full textScopusBritish Machine Vision Conference, BMVC 2011, Dundee, UK, August 29 - September 2, 2011. Proceedings

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