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     Research Journal of Applied Sciences, Engineering and Technology


Analysis and Reliability Performance Comparison of Different Facial Image Features

1J. Madhavan and 2K. Porkumaran
1Department of ECE, Adhiyamaan College of Engineering, Hosur, India
2Dr. NGP Institute of Technology, Coimbatore, India
Research Journal of Applied Sciences, Engineering and Technology  2014  18:1973-1979
http://dx.doi.org/10.19026/rjaset.8.1189  |  © The Author(s) 2014
Received: August ‎22, ‎2014  |  Accepted: October ‎11, 2014  |  Published: November 15, 2014

Abstract

This study performs reliability analysis on the different facial features with weighted retrieval accuracy on increasing facial database images. There are many methods analyzed in the existing papers with constant facial databases mentioned in the literature review. There were not much work carried out to study the performance in terms of reliability and also how the method will perform on increasing the size of the database. In this study certain feature extraction methods were analyzed on the regular performance measure and also the performance measures are modified to fit the real time requirements by giving weight ages for the closer matches. In this study four facial feature extraction methods are performed, they are DWT with PCA, LWT with PCA, HMM with SVD and Gabor wavelet with HMM. Reliability of these methods are analyzed and reported. Among all these methods Gabor wavelet with HMM gives more reliability than other three methods performed. Experiments are carried out to evaluate the proposed approach on the Olivetti Research Laboratory (ORL) face database.

Keywords:

DWT, gabor filter, HMM, LWT, PCA,


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Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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