A Novel Evolutionary Face Recognition Algorithm Using Particle Swarm Optimization

In this paper a novel algorithm to solve the problem of automatic face recognition is presented. The novelty of the algorithm is the ability to combine the computer vision tasks with Particle Swarm Optimization (PSO) to improve the execution time and to obtain better recognition results. The crucial...

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Bibliographic Details
Published in:	2009 Fifth International Conference on Signal Image Technology and Internet Based Systems pp. 42 - 47
Main Authors:	Villegas, Osslan Osiris Vergara, Quintero, Mitzel Aviles, Sanchez, Vianey Guadalupe Cruz, de Jesus Ochoa Dominguez, Humberto
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-11-2009
Subjects:	Algorithm design and analysis Computer vision Eigenfaces Evolutionary Computer Vision Face Face recognition Image recognition Particle swarm optimization Training
Online Access:	Get full text
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Summary:	In this paper a novel algorithm to solve the problem of automatic face recognition is presented. The novelty of the algorithm is the ability to combine the computer vision tasks with Particle Swarm Optimization (PSO) to improve the execution time and to obtain better recognition results. The crucial stage of a typical system of face recognition is improved by using a fitness function to measure the similarity of an input face compared with a database of faces. The use of the fitness function helps to obtain more accurate results in a faster way. The results obtained are excellent even when the system was tested in uncontrolled environments. A comparison of the results obtained with the algorithm without PSO versus the algorithm using PSO is also presented.
ISBN:	9781424457403 1424457408
DOI:	10.1109/SITIS.2009.17