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Face Authentication using Euclidean Distance Model with PSO Algorithm

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IJCA Proceedings on International Conference on Innovations in Computing Techniques (ICICT 2015)
© 2015 by IJCA Journal
ICICT 2015 - Number 3
Year of Publication: 2015
Authors:
R. Senthilkumar
M. Vigneshwaran
S. Uma

R.senthilkumar, M.vigneshwaran and S.uma. Article: Face Authentication using Euclidean Distance Model with PSO Algorithm. IJCA Proceedings on International Conference on Innovations in Computing Techniques (ICICT 2015) ICICT 2015(3):16-19, July 2015. Full text available. BibTeX

@article{key:article,
	author = {R.senthilkumar and M.vigneshwaran and S.uma},
	title = {Article: Face Authentication using Euclidean Distance Model with PSO Algorithm},
	journal = {IJCA Proceedings on International Conference on Innovations in Computing Techniques (ICICT 2015)},
	year = {2015},
	volume = {ICICT 2015},
	number = {3},
	pages = {16-19},
	month = {July},
	note = {Full text available}
}

Abstract

In recent technological world lot of devices are invented. Moreover the focused topic is on security system. Even with lot of security system like finger print based, eye-retina based, pin-code based systems are available, face recognition based security system has vital role of advanced technology. Feature based Face authentication requires feature extraction, feature selection and classification. Face recognition process performance is mainly depended on the selection of such extractor and classifier. Feature extraction process gives the feature points of the face in the image. From that important fiducial points are extracted using feature selection process. One have to reduce the feature points, in order to obtain the fast response of recognition. In this work we proposed the feature extraction process with Gabor filter where it is convenient as a biometric filter. Before going to verification process face localization is important one, then only we can reduce the unnecessary feature points. This is done by Neural network classifier. After the face image is obtained, we go for the authentication process with modified Euclidean distance of each fiducial points that made coefficient model for each person. Best optimized Euclidean distance coefficient of images are obtrained through PSO algorithm. Thus the coefficient of test and trained images are given to the classifier and the minimum mean difference profile made as the matching profile. By this work, we reduced the perception time at significant level compared to previous work and we made the recognition rate of the work as 91. 81 percent with PIE database.

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