PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning based automatic face annotation for arbitrary poses and expressions from frontal images only
Akshay Asthana, Roland Goecke, Novi Quadrianto and Tom Gedeon
In: 22nd IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 20-25 June 2009, Fontainebleau Resort, Miami Beach, Florida.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:5557
Deposited By:Novi Quadrianto
Deposited On:25 February 2010