[CVPR2007] Monocular and Stereo Methods for AAM Learning from Video

Title : Monocular and Stereo Methods for AAM Learning from Video

Author :

Jason Saragih @ Research School of Information Sciences and Engineering, Australian National University
Roland Goecke @ National ICT Australia, Canberra Research Laboratory Canberra, Australia

Abstract :

The active appearance model (AAM) is a powerful method for modeling deformable visual objects. One of the major drawbacks of the AAM is that it requires a training set of pseudo-dense correspondences over the whole database. In this work, we investigate the utility of stereo constraints for automatic model building from video. First, we propose a new method for automatic correspondence finding in monocular images which is based on an adaptive template tracking paradigm. We then extend this method to take the scene geometry into account, proposing three approaches, each accounting for the availability of the fundamental matrix and calibration parameters or the lack thereof. The performance of the monocular method was first evaluated on a pre-annotated database of a talking face. We then compared the monocularmethod against its three stereo extensions using a stereo database.