Learning Based Automatic Face Annotation for Arbitrary Poses and Expressions from Frontal Images Only

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 annota...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Akshay Asthana (Australian National University), Roland Goecke (Australian National University), Novi Quadrianto (Australian National University & SML-NICTA), Tom Gedeon (Australian National University)
• Keywords: Automatic Face Annotation, Computer Vision, CVPR 2009, Poses And Expressions