3D face recognition based on high-resolution 3D face modeling from frontal and profile views
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Face modeling using grid light and feature point extraction
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and its Applications - Volume Part I
Face modeling and wrinkle simulation using convolution surface
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
3-D face modeling from two views and grid light
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Automatic 3d face model reconstruction using one image
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
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We present an algorithm for 3-D face modeling from a frontal and a profile view images of a person's face. The algorithm starts by computing the 3D coordinates of automatically extracted facial feature points. The coordinates of the selected feature points are then used to deform a 3D generic face model to obtain a 3D face model for that person. Procrustes analysis is used to globally minimize the distance between facial feature vertices in the model and the corresponding 3D points obtained from the images. Then, local deformation is performed on the facial feature vertices to obtain a more realistic 3D model for the person. Preliminary experiments to asses the applicability of the models for face recognition show encouraging results.