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Simplified and efficient face recognition system on real image set and synthesised data

Simplified and efficient face recognition system on real image set and synthesised data This paper presents experimental results related to a simplified and efficient face recognition system using a basic webcam. More specifically, the purpose of this work is two-fold: 1) to detect constantly the individual's face in front of the computer, in an uncontrolled environment; 2) to send an alert to the system manager if another individual's face is recognised. Sixteen distances based on 14 points of the face between eyes, nose and mouth are considered. Experimental results carried out on two different training sets are presented. The first database has been constructed on 500 real face images of ten individuals (50 faces each one), while the second database has been created on the same 500 previous images and 500 new synthetic data obtained through a crossover operation. For experimental evaluation, a 5-NN classifier is used. Finally, results show the performance in terms of a new correlation score for the recognition task. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

Simplified and efficient face recognition system on real image set and synthesised data

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2017.085678
Publisher site
See Article on Publisher Site

Abstract

This paper presents experimental results related to a simplified and efficient face recognition system using a basic webcam. More specifically, the purpose of this work is two-fold: 1) to detect constantly the individual's face in front of the computer, in an uncontrolled environment; 2) to send an alert to the system manager if another individual's face is recognised. Sixteen distances based on 14 points of the face between eyes, nose and mouth are considered. Experimental results carried out on two different training sets are presented. The first database has been constructed on 500 real face images of ten individuals (50 faces each one), while the second database has been created on the same 500 previous images and 500 new synthetic data obtained through a crossover operation. For experimental evaluation, a 5-NN classifier is used. Finally, results show the performance in terms of a new correlation score for the recognition task.

Journal

International Journal of BiometricsInderscience Publishers

Published: Jan 1, 2017

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