A unified tensor framework for face recognition

In this paper we propose a new optimization framework that unites some of the existing tensor based methods for face recognition on a common mathematical basis. Tensor based approaches rely on the ability to decompose an image into its constituent factors (i.e. person, lighting, viewpoint, etc.) and...

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Main Authors: Rana, Santu, Liu, Wan-quan, Lazarescu, Mihai, Venkatesh, Svetha
Format: Journal Article
Published: Elsevier Science 2009
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/38703
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author Rana, Santu
Liu, Wan-quan
Lazarescu, Mihai
Venkatesh, Svetha
author_facet Rana, Santu
Liu, Wan-quan
Lazarescu, Mihai
Venkatesh, Svetha
author_sort Rana, Santu
building Curtin Institutional Repository
collection Online Access
description In this paper we propose a new optimization framework that unites some of the existing tensor based methods for face recognition on a common mathematical basis. Tensor based approaches rely on the ability to decompose an image into its constituent factors (i.e. person, lighting, viewpoint, etc.) and then utilizing these factor spaces for recognition. We first develop a multilinear optimization problem relating an image to its constituent factors and then develop our framework by formulating a set of strategies that can be followed to solve this optimization problem. The novelty of our research is that the proposed framework offers an effective methodology for explicit non-empirical comparison of the different tensor methods as well as providing a way to determine the applicability of these methods in respect to different recognition scenarios. Importantly, the framework allows the comparative analysis on the basis of quality of solutions offered by these methods. Our theoretical contribution has been validated by extensive experimental results using four benchmark datasets which we present along with a detailed discussion.
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institution Curtin University Malaysia
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publishDate 2009
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spelling curtin-20.500.11937-387032017-09-13T15:58:41Z A unified tensor framework for face recognition Rana, Santu Liu, Wan-quan Lazarescu, Mihai Venkatesh, Svetha Tensor Multilinear algebra Face recognition In this paper we propose a new optimization framework that unites some of the existing tensor based methods for face recognition on a common mathematical basis. Tensor based approaches rely on the ability to decompose an image into its constituent factors (i.e. person, lighting, viewpoint, etc.) and then utilizing these factor spaces for recognition. We first develop a multilinear optimization problem relating an image to its constituent factors and then develop our framework by formulating a set of strategies that can be followed to solve this optimization problem. The novelty of our research is that the proposed framework offers an effective methodology for explicit non-empirical comparison of the different tensor methods as well as providing a way to determine the applicability of these methods in respect to different recognition scenarios. Importantly, the framework allows the comparative analysis on the basis of quality of solutions offered by these methods. Our theoretical contribution has been validated by extensive experimental results using four benchmark datasets which we present along with a detailed discussion. 2009 Journal Article http://hdl.handle.net/20.500.11937/38703 10.1016/j.patcog.2009.03.018 Elsevier Science restricted
spellingShingle Tensor
Multilinear algebra
Face recognition
Rana, Santu
Liu, Wan-quan
Lazarescu, Mihai
Venkatesh, Svetha
A unified tensor framework for face recognition
title A unified tensor framework for face recognition
title_full A unified tensor framework for face recognition
title_fullStr A unified tensor framework for face recognition
title_full_unstemmed A unified tensor framework for face recognition
title_short A unified tensor framework for face recognition
title_sort unified tensor framework for face recognition
topic Tensor
Multilinear algebra
Face recognition
url http://hdl.handle.net/20.500.11937/38703