A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
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
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical Method for Robust 3D Surface Reconstruction from Sparse Data
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Minimum Variance Estimation of 3D Face Shape from Multi-view
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Recovering Facial Shape Using a Statistical Model of Surface Normal Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of 3D Face Reconstruction
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
3D Face Reconstruction from 2D Images
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
Efficient 3D reconstruction for face recognition
Pattern Recognition
The 3D Chinese head and face modeling
Computer-Aided Design
Shape from recognition: a novel approach for 3-D face shape recovery
IEEE Transactions on Image Processing
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This paper aims to test the regularized 3D face shape reconstruction algorithm to find out how the feature points selection affect the accuracy of the 3D face reconstruction based on the PCA-model. A case study on USF Human ID 3D database has been used to study these effect. We found that, if the test face is from the training set, then any set of any number greater than or equal to the number of training faces can reconstruct exact 3D face. If the test face does not belong to the training set, it will hardly reconstruct the exact 3D face using 3D PCA-based models. However, it could reconstruct an approximate face shape depending on the number of feature points and the weighting factor. Furthermore, the accuracy of reconstruction by a large number of feature points ( 150) is relatively the same in all cases even with different locations of points on the face. The regularized algorithm has also been tested to reconstruct 3D face shapes from a number of feature points selected manually from real 2D face images. Some 2D images from CMU-PIE database have been used to visualize the resulted 3D face shapes.