Maghari, Ashraf Y. A.
(2014)
Adaptive Pca-Based Models To
Reconstruct 3d Faces From Single 2d
Images.
PhD thesis, Universiti Sains Malaysia.
Abstract
Example-based statistical face models using Principle Component Analysis (PCA) have
been widely used for 3D face reconstruction and face recognition. The main concern of this
thesis is to improve the accuracy and the efficiency of the PCA-based 3D face shape reconstruction.
More precisely, this thesis addresses the challenge of increasing the Representational
Power (RP) of the PCA-based model in accordance with the encouraging results of the conducted
empirical study. A limited set of training data is utilized towards enhancing the accuracy
of 3D reconstruction. Concerning the empirical study, it examines the effect of phenomenal
factors (i.e. size of the training set and the variation of the selected training examples) on the
RP of 3D PCA-based face models. A regularized 3D face reconstruction algorithm has also
been examined to find out how common factors such as the regularization matrix, the number
of feature points, and the regularization parameter l affect the accuracy of the 3D face
reconstruction based on the PCA model.
Importantly, an adaptive PCA-based model is proposed to increase the RP of the 3D face
reconstruction model by deforming a set of examples in the training dataset. By adding these
deformed samples together with the original training samples, it has been shown that the improvement
in the RP can be achieved. Comprehensive experimental validations have been carried
out to demonstrate that the proposed model considerably improves the RP of the standard
PCA-based model with reduced face shape reconstruction errors.
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