Learning based automatic face annotation for arbitrary poses and expressions from frontal images only

Asthana, Akshay, Goecke, Roland, Quadrianto, Novi and Gedeon, Tom (2009) Learning based automatic face annotation for arbitrary poses and expressions from frontal images only. Published in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Florida, USA; 20-25 June 2009. Institute of Electrical and Electronics Engineers ISSN 1063-6919 ISBN 9781424439928

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Abstract

Statistical approaches for building non-rigid deformable models, such as the active appearance model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases.

Item Type: Conference Proceedings
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > Q Science (General)
Depositing User: Novi Quadrianto
Date Deposited: 24 Feb 2014 11:51
Last Modified: 16 Jun 2017 15:30
URI: http://sro.sussex.ac.uk/id/eprint/47606

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