Pose-Independent Object Representation by 2-D Views
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Multi-view face segmentation using fusion of statistical shape and appearance models
Computer Vision and Image Understanding
Face hallucination with pose variation
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
3D human face soft tissues landmarking method: An advanced approach
Computers in Industry
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A method of manifold representation for human faces with pose variations is proposed. Our model consists of mappings between 3D head angles and facial images separately represented in shape and texture, via sub-space models spanned by principal components (PCs). Explicit mappings to and from 3D head angles are used as processes of pose estimation and transformation, respectively. Generalization capability to unknown head poses enables our model to continuously cover pose parameter space, providing high approximation accuracy. The feasibility of this model is evaluated in a number of experiments. We also propose a novel pose-invariant face recognition system using our model as the entry format for a gallery of known persons. Experimental results with 3D facial models recorded by a Cyberware scanner show that our model provides a superior recognition performance against pose variations, and that texture synthesis process is carried out correctly.