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Reseach Article

Feature and Decision Fusion based Facial Recognition in Challenging Environment

Published on None 2011 by Md. Rabiul Islam, Md. Fayzur Rahman
Artificial Intelligence Techniques - Novel Approaches & Practical Applications
Foundation of Computer Science USA
AIT - Number 1
None 2011
Authors: Md. Rabiul Islam, Md. Fayzur Rahman
03530cf2-16be-444e-a3e0-ea9ad90698b3

Md. Rabiul Islam, Md. Fayzur Rahman . Feature and Decision Fusion based Facial Recognition in Challenging Environment. Artificial Intelligence Techniques - Novel Approaches & Practical Applications. AIT, 1 (None 2011), 30-35.

@article{
author = { Md. Rabiul Islam, Md. Fayzur Rahman },
title = { Feature and Decision Fusion based Facial Recognition in Challenging Environment },
journal = { Artificial Intelligence Techniques - Novel Approaches & Practical Applications },
issue_date = { None 2011 },
volume = { AIT },
number = { 1 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 30-35 },
numpages = 6,
url = { /specialissues/ait/number1/2822-202/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%A Md. Rabiul Islam
%A Md. Fayzur Rahman
%T Feature and Decision Fusion based Facial Recognition in Challenging Environment
%J Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%@ 0975-8887
%V AIT
%N 1
%P 30-35
%D 2011
%I International Journal of Computer Applications
Abstract

This paper introduces a face recognition system that contributes the feature and decision fusion in challenging environment. In this work, we investigate the proposed facial recognition system in typical office environments conditions. Though the traditional HMM based facial recognition system is very sensitive to the facial parameters variation, the proposed feature and decision fusion based face recognition is found to be stance and performs well for improving the robustness and naturalness of human-computer-interaction. At first appearance and shape based features are extracted using Active Appearance Model and Active Shape Model. The other task combines appearance and shape based features that have been used by the multiple Discrete Hidden Markov Model classifiers with likelihood ratio based score fusion and majority voting method. The performances of all these uni-modal and multi-modal system performance have been evaluated and compared with each other according to the VALID database.

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Index Terms

Computer Science
Information Sciences

Keywords

Face Recognition Feature and Decision Fusion Facial Feature Extraction Human Computer Interaction Discrete Hidden Markov Model