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A Heuristic based RBFN for Location and Rotation Invariant Clear and Occluded Face Identification

Published on March 2014 by Goutam Sarker, Shruti Sharma
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International Conference on Advances in Computer Engineering and Applications
Foundation of Computer Science USA
ICACEA - Number 1
March 2014
Authors: Goutam Sarker, Shruti Sharma
6f97aeb7-16f9-4cdb-8899-a144652f028a

Goutam Sarker, Shruti Sharma . A Heuristic based RBFN for Location and Rotation Invariant Clear and Occluded Face Identification. International Conference on Advances in Computer Engineering and Applications. ICACEA, 1 (March 2014), 30-36.

@article{
author = { Goutam Sarker, Shruti Sharma },
title = { A Heuristic based RBFN for Location and Rotation Invariant Clear and Occluded Face Identification },
journal = { International Conference on Advances in Computer Engineering and Applications },
issue_date = { March 2014 },
volume = { ICACEA },
number = { 1 },
month = { March },
year = { 2014 },
issn = 0975-8887,
pages = { 30-36 },
numpages = 7,
url = { /proceedings/icacea/number1/15613-1401/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Computer Engineering and Applications
%A Goutam Sarker
%A Shruti Sharma
%T A Heuristic based RBFN for Location and Rotation Invariant Clear and Occluded Face Identification
%J International Conference on Advances in Computer Engineering and Applications
%@ 0975-8887
%V ICACEA
%N 1
%P 30-36
%D 2014
%I International Journal of Computer Applications
Abstract

This paper describes a robust and efficient method for rotation and location independent identification and localization of facial images using one modified Radial Basis Function Network (RBFN) which embeds a new Heuristic Based Clustering (HBC) and Back Propagation (BP) learning. HBC in RBFN determines the natural number of clusters or groups on the basis of 'person-view'. BP network learns to identify a 'person' irrespective of his view. The method successfully performs location invariant upright and rotated facial identification in different views and expressions with or without occlusion. The learning as well as identification with standard facial database is fast, efficient, effective and the accuracy as well as precision of the system with Holdout Method is moderate.

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Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning Hbc Bp Network Rbfn Holdout Method Accuracy