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Optimised Landmark Model Matching for Face Recognition

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FGR.2006.85

Seventh IEEE International Conference ...

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	ASCII Text	x
	Rajinda Senaratne, Saman Halgamuge, "Optimised Landmark Model Matching for Face Recognition," Automatic Face and Gesture Recognition, IEEE International Conference on, vol. 0, no. 0, pp. 120-125, Seventh IEEE International Conference on Automatic Face and Gesture Recognition (FG'06), 2006.

	BibTex	x
	@article{ 10.1109/FGR.2006.85, author = {Rajinda Senaratne and Saman Halgamuge}, title = {Optimised Landmark Model Matching for Face Recognition}, journal ={Automatic Face and Gesture Recognition, IEEE International Conference on}, volume = {0}, year = {2006}, isbn = {}, pages = {120-125}, doi = {http://doi.ieeecomputersociety.org/10.1109/FGR.2006.85}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Automatic Face and Gesture Recognition, IEEE International Conference on TI - Optimised Landmark Model Matching for Face Recognition SN - SP120 EP125 A1 - Rajinda Senaratne, A1 - Saman Halgamuge, PY - 2006 KW - null VL - 0 JA - Automatic Face and Gesture Recognition, IEEE International Conference on ER -

Rajinda Senaratne, The University of Melbourne, Australia

Saman Halgamuge, The University of Melbourne, Australia

A new method for face recognition, Landmark Model Matching, is proposed in this paper. It is based on the concepts of Elastic Bunch Graph Matching and Active Shape Model, and optimised with Particle Swarm Optimisation. It is a fully automatic algorithm and can be used for face databases where only one image per person is available. A face is represented by a Landmark Model consisting of nodes labelled with jets and gray-level profiles. A Landmark Distribution Model is created from a few training images. The model similarity between the Landmark Distribution Model and the deformable Landmark Model that has to be fitted to the face in the image is maximised by Particle Swarm Optimisation, to find the optimal model to represent the face. Improved results were obtained by this method compared with Elastic Bunch Graph Matching without Optimisation.

Citation:

Rajinda Senaratne, Saman Halgamuge, "Optimised Landmark Model Matching for Face Recognition," fg,pp.120-125, Seventh IEEE International Conference on Automatic Face and Gesture Recognition (FG'06), 2006

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