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3D Constrained Local Model for rigid and non-rigid facial tracking

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3 Author(s)

We present 3D Constrained Local Model (CLM-Z) for robust facial feature tracking under varying pose. Our approach integrates both depth and intensity information in a common framework. We show the benefit of our CLM-Z method in both accuracy and convergence rates over regular CLM formulation through experiments on publicly available datasets. Additionally, we demonstrate a way to combine a rigid head pose tracker with CLM-Z that benefits rigid head tracking. We show better performance than the current state-of-the-art approaches in head pose tracking with our extension of the generalised adaptive view-based appearance model (GAVAM).

Published in:

Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
Biometrics Compendium, IEEE

Date of Conference:

16-21 June 2012