Biologically inspired framework for pose invariance in visual face recognition

Invariant recognition is the ability to recognize an object regardless of how it is transformed given that the transformation is allowable. A small change in image will cause a dramatic change in signals. Visual system needs to be able to perform recognition without being influenced by these signal...

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Main Author:	Tay, Noel Nuo Wi
Format:	Thesis
Published:	2012
Subjects:	TA Engineering (General) Civil engineering (General)
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id	my-mmu-ep.5443
record_format	uketd_dc
spelling	my-mmu-ep.54432014-04-22T06:59:03Z Biologically inspired framework for pose invariance in visual face recognition 2012-02 Tay, Noel Nuo Wi TA Engineering (General). Civil engineering (General) Invariant recognition is the ability to recognize an object regardless of how it is transformed given that the transformation is allowable. A small change in image will cause a dramatic change in signals. Visual system needs to be able to perform recognition without being influenced by these signal variations. There are contradictory but justified different views on how invariance is achieved, yet the problem is still considered unsolved. For this research work, a biological-inspired framework is built upon these views and findings to achieve invariance in facial recognition. Biologically linked framework is preferred since human visual system is far superior compared to existing artificial visual systems, but little is known about it due to its complexity. Thus, it is the intention of the researcher to develop a framework based on biology that acts as a guide for further studies. Due to the broad scope of the field of biological visual system, the framework is built based on problems on 2D translation and scaling invariance and 3D pose invariance. Therefore, the motivation is to build a framework to provide insights into solving translation, scaling and 3D pose invariance problems. The framework is first built through the compilation of related literatures. It can be divided into early/primary and secondary visual stages. Primary visual stage models the visual pathway from retina to the striate cortex (V1), whereas the modelling of secondary visual stage is mainly based on current psychophysical evidences. Early visual stages employ simple and complex cells to provide translation and scaling invariance. Signals handled at this primary stage are information-rich but fragmented. Subsequent stages employ geometrical transformation to achieve 3D pose invariance, where the signals are integrated but in an abstract form. A link between the primary and secondary stage is established through appropriate dimension reduction which is biologically justified. 2012-02 Thesis http://shdl.mmu.edu.my/5443/ http://library.mmu.edu.my/diglib/onlinedb/dig_lib.php masters Multimedia University Faculty of Information Science and Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Tay, Noel Nuo Wi Biologically inspired framework for pose invariance in visual face recognition
description	Invariant recognition is the ability to recognize an object regardless of how it is transformed given that the transformation is allowable. A small change in image will cause a dramatic change in signals. Visual system needs to be able to perform recognition without being influenced by these signal variations. There are contradictory but justified different views on how invariance is achieved, yet the problem is still considered unsolved. For this research work, a biological-inspired framework is built upon these views and findings to achieve invariance in facial recognition. Biologically linked framework is preferred since human visual system is far superior compared to existing artificial visual systems, but little is known about it due to its complexity. Thus, it is the intention of the researcher to develop a framework based on biology that acts as a guide for further studies. Due to the broad scope of the field of biological visual system, the framework is built based on problems on 2D translation and scaling invariance and 3D pose invariance. Therefore, the motivation is to build a framework to provide insights into solving translation, scaling and 3D pose invariance problems. The framework is first built through the compilation of related literatures. It can be divided into early/primary and secondary visual stages. Primary visual stage models the visual pathway from retina to the striate cortex (V1), whereas the modelling of secondary visual stage is mainly based on current psychophysical evidences. Early visual stages employ simple and complex cells to provide translation and scaling invariance. Signals handled at this primary stage are information-rich but fragmented. Subsequent stages employ geometrical transformation to achieve 3D pose invariance, where the signals are integrated but in an abstract form. A link between the primary and secondary stage is established through appropriate dimension reduction which is biologically justified.
format	Thesis
qualification_level	Master's degree
author	Tay, Noel Nuo Wi
author_facet	Tay, Noel Nuo Wi
author_sort	Tay, Noel Nuo Wi
title	Biologically inspired framework for pose invariance in visual face recognition
title_short	Biologically inspired framework for pose invariance in visual face recognition
title_full	Biologically inspired framework for pose invariance in visual face recognition
title_fullStr	Biologically inspired framework for pose invariance in visual face recognition
title_full_unstemmed	Biologically inspired framework for pose invariance in visual face recognition
title_sort	biologically inspired framework for pose invariance in visual face recognition
granting_institution	Multimedia University
granting_department	Faculty of Information Science and Technology
publishDate	2012
_version_	1747829575805566976

Biologically inspired framework for pose invariance in visual face recognition

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