A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
High-Performance Rotation Invariant Multiview Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose Robust Face Tracking by Combining Active Appearance Models and Cylinder Head Models
International Journal of Computer Vision
Real-time combined 2D+3D active appearance models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
3D facial feature localization for registration
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Real time head pose estimation with random regression forests
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Active Appearance Models (AAMs) are widely used to match a shape and appearance model to an image. This paper extends the commonly used 2D shape model to 3D, and introduces an effective method for integrating alignment to RGB and 3D range images. The use of a three dimensional model allows accurate estimation of head orientation, shape and position. Existing approaches combining range and intensity data use a manually tuned weighting function to balance 2D and 3D alignments. We develop a method to guide the alignment based on the observed image properties and the sensor characteristics. Our approach is experimentally validated using two different sets of depth and RGB cameras. In our experiments we achieve stable alignment under wide angular head rotations of up to 80^o with a maximum improvement of 26% compared to the 3D AAM using intensity image and 30% improvement over the state-of-the-art 3DMM methods in terms of 3D head pose estimation.