3D Face Analysis using Tensor Approach

The advancement of multimodal technology has enabled the creation of large face datasets. Multidimensional characteristics such as covariates and multimodal aspects in 2D, 2.5D, and 3D data are included in these datasets. Early studies in face research used matrix-based and vector-based algorithms t...

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Main Author:	Suriani, Ab Rahman
Format:	Thesis
Language:	English
Published:	2022
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/38306/1/Suriani.pdf
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spelling	my-unimas-ir.383062023-07-04T06:03:22Z 3D Face Analysis using Tensor Approach 2022 Suriani, Ab Rahman QA75 Electronic computers. Computer science The advancement of multimodal technology has enabled the creation of large face datasets. Multidimensional characteristics such as covariates and multimodal aspects in 2D, 2.5D, and 3D data are included in these datasets. Early studies in face research used matrix-based and vector-based algorithms to represent faces. According to studies, these methods have the potential to prevent the loss of critical and significant data, which could lead to lower recognition performance. The goal of this research is to develop and validate a tensor-based face recognition method that can overcome the drawbacks of matrix-based Principal Component Analysis (PCA). A face dataset consists of faces with a combination of multiple underlying causal factors such as facial expression, expression intensity, angle of view, gender and race, and the bilinear technique alone is incapable of accurately representing the dataset’s multidimensionality. PCA’s shortcomings can be overcome by using the tensor decomposition approach to separate these distinct variations. Experimental results have shown that the multilinear tensor approach could statistically outperform the bilinear PCA approach in face recognition applications. This study has added to the understanding of the centring strategy used in tensor models. The median projection operator could maximise the variation for each principal axis in the tensor space and the results have shown that in the median-centred strategy, recognition rates for emotional expressions increased from 0.4 percent to 1.4 percent. Only fear expressions have demonstrated similar recognition performance in mean-centred and median-centred experiments. Current face recognition using PCA is unable to distinguish between different types of centring approaches and cluster them. As a result, this research adopted a hybrid combined framework of a tensor model with an ANOVA (Analysis of Variance) model to uncover the substantial effects of within-subject differences (expression types and expression strengths) on recognition performance. Apart from that, experimental results revealed an interaction effect between those two covariates, showing that the effect of expression types on recognition performance is not continuous and is influenced by intensity levels. This new evidence may well be useful in a variety of recognition processes. Universiti Malaysia Sarawak 2022 Thesis http://ir.unimas.my/id/eprint/38306/ http://ir.unimas.my/id/eprint/38306/1/Suriani.pdf text en validuser phd doctoral Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Suriani, Ab Rahman 3D Face Analysis using Tensor Approach
description	The advancement of multimodal technology has enabled the creation of large face datasets. Multidimensional characteristics such as covariates and multimodal aspects in 2D, 2.5D, and 3D data are included in these datasets. Early studies in face research used matrix-based and vector-based algorithms to represent faces. According to studies, these methods have the potential to prevent the loss of critical and significant data, which could lead to lower recognition performance. The goal of this research is to develop and validate a tensor-based face recognition method that can overcome the drawbacks of matrix-based Principal Component Analysis (PCA). A face dataset consists of faces with a combination of multiple underlying causal factors such as facial expression, expression intensity, angle of view, gender and race, and the bilinear technique alone is incapable of accurately representing the dataset’s multidimensionality. PCA’s shortcomings can be overcome by using the tensor decomposition approach to separate these distinct variations. Experimental results have shown that the multilinear tensor approach could statistically outperform the bilinear PCA approach in face recognition applications. This study has added to the understanding of the centring strategy used in tensor models. The median projection operator could maximise the variation for each principal axis in the tensor space and the results have shown that in the median-centred strategy, recognition rates for emotional expressions increased from 0.4 percent to 1.4 percent. Only fear expressions have demonstrated similar recognition performance in mean-centred and median-centred experiments. Current face recognition using PCA is unable to distinguish between different types of centring approaches and cluster them. As a result, this research adopted a hybrid combined framework of a tensor model with an ANOVA (Analysis of Variance) model to uncover the substantial effects of within-subject differences (expression types and expression strengths) on recognition performance. Apart from that, experimental results revealed an interaction effect between those two covariates, showing that the effect of expression types on recognition performance is not continuous and is influenced by intensity levels. This new evidence may well be useful in a variety of recognition processes.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Suriani, Ab Rahman
author_facet	Suriani, Ab Rahman
author_sort	Suriani, Ab Rahman
title	3D Face Analysis using Tensor Approach
title_short	3D Face Analysis using Tensor Approach
title_full	3D Face Analysis using Tensor Approach
title_fullStr	3D Face Analysis using Tensor Approach
title_full_unstemmed	3D Face Analysis using Tensor Approach
title_sort	3d face analysis using tensor approach
granting_institution	Universiti Malaysia Sarawak
granting_department	Faculty of Computer Science and Information Technology
publishDate	2022
url	http://ir.unimas.my/id/eprint/38306/1/Suriani.pdf
_version_	1783728498747113472

3D Face Analysis using Tensor Approach

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