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

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 t...

全面介绍

Saved in:
书目详细资料
主要作者: Maghari, Ashraf Y. A.
格式: Thesis
语言:English
出版: 2014
主题:
在线阅读:http://eprints.usm.my/49017/1/ASHRAF%20Y.%20A.%20MAGHARI_HJ.pdf
标签: 添加标签
没有标签, 成为第一个标记此记录!
id my-usm-ep.49017
record_format uketd_dc
spelling my-usm-ep.490172021-04-26T07:30:15Z Adaptive Pca-Based Models To Reconstruct 3d Faces From Single 2d Images 2014-03 Maghari, Ashraf Y. A. QA75.5-76.95 Electronic computers. Computer science 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. 2014-03 Thesis http://eprints.usm.my/49017/ http://eprints.usm.my/49017/1/ASHRAF%20Y.%20A.%20MAGHARI_HJ.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution Universiti Sains Malaysia
collection USM Institutional Repository
language English
topic QA75.5-76.95 Electronic computers
Computer science
spellingShingle QA75.5-76.95 Electronic computers
Computer science
Maghari, Ashraf Y. A.
Adaptive Pca-Based Models To Reconstruct 3d Faces From Single 2d Images
description 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.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Maghari, Ashraf Y. A.
author_facet Maghari, Ashraf Y. A.
author_sort Maghari, Ashraf Y. A.
title Adaptive Pca-Based Models To Reconstruct 3d Faces From Single 2d Images
title_short Adaptive Pca-Based Models To Reconstruct 3d Faces From Single 2d Images
title_full Adaptive Pca-Based Models To Reconstruct 3d Faces From Single 2d Images
title_fullStr Adaptive Pca-Based Models To Reconstruct 3d Faces From Single 2d Images
title_full_unstemmed Adaptive Pca-Based Models To Reconstruct 3d Faces From Single 2d Images
title_sort adaptive pca-based models to reconstruct 3d faces from single 2d images
granting_institution Universiti Sains Malaysia
granting_department Pusat Pengajian Sains Komputer
publishDate 2014
url http://eprints.usm.my/49017/1/ASHRAF%20Y.%20A.%20MAGHARI_HJ.pdf
_version_ 1747821976343281664