Adaptive Pca-Based Models To Reconstruct 3d Faces From Single 2d Images

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Date
2014-03
Authors
Ashraf Y. A. Maghari
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Publisher
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. Furthermore, it has been justified that the adaptive PCA-based model is capable of reconstructing 3D face images by retaining facial expressions, although the training samples contained only neutral expression. To optimize the selection of regularization parameter (l), a distance-based model is proposed to automatically find an appropriate value of l, and therefore, it responds to the requirements of the fitting stage. The proposed distance-based model is evaluated by comparing the automatically determined l with the pre-calculated best one. Moreover, examples of reconstructed 3D face shapes are visualized to clarify the robustness of the proposed distance-based model. Then by warping the 2D texture to the reconstructed face shape 3D face reconstruction is achieved. For the texture warping, the 2D face deformation is learned from the model texture using a set of facial landmarks. Finally experimental evaluations have been demonstrated to show that the overall proposed system which comprises of the adaptive PCA-based model and the distance-based model outperforms some of the recent approaches in terms of efficiency. Furthermore, it is shown that the proposed models could contribute to several related studies in the field of image reconstruction.
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Keywords
Three-dimensional imaging
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