Synthesizing realistic facial expressions from photographs
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Animated heads from ordinary images: a least-squares approach
Computer Vision and Image Understanding
Building 3-D Human Face Models from Two Photographs
Journal of VLSI Signal Processing Systems - Special issue on multimedia signal processing
Estimating facial pose using shape-from-shading
Pattern Recognition Letters
3-D Face Modeling Using Two Views and a Generic Face Model with Application to 3-D Face Recognition
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Deformable Model-Based Shape and Motion Analysis from Images Using Motion Residual Error
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Multi-View AAM Fitting and Camera Calibration
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Video-rate capture of dynamic face shape and appearance
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Facial model estimation from stereo/mono image sequence
IEEE Transactions on Multimedia
3D face verification using a free-parts approach
Pattern Recognition Letters
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This paper examines the generation of a generic face model from a moderate sized database. Generic models of a human face have been used in a number of computer vision fields, including reconstruction, active appearance model fitting and face recognition. The model that is constructed in this paper is based upon the mean of the squared errors that are generated by comparing average faces that are calculated from two independent and random samplings of a database of 3D range images. This information is used to determine the average amount of error that is present at given height locations along the generic face based on the number of samples that are considered. These results are then used to sub-region the generic face into areas where the greatest variations occur in the generic face models.