Effect of Facial Feature Points Selection on 3D Face Shape Reconstruction Using Regularization

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.