Shaker K. Ali; Zainab Naser Azeez; Ahmed Abdul-Hussein Ouda

A New Clustering Algorithm for Face Classification

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Author(s)

Shaker K. Ali ^1,* Zainab Naser Azeez ¹ Ahmed Abdul-Hussein Ouda ¹

1. Department of computer science University of Thi-Qar, Thi-Qar, Iraq

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2016.06.01

Received: 6 Sep. 2015 / Revised: 11 Dec. 2015 / Accepted: 28 Feb. 2016 / Published: 8 Jun. 2016

Index Terms

Biometric, Clustering, distance_Euclidean, distance_correlation, distance_Minkowski, face recognition

Abstract

In This paper, we proposed new clustering algorithm depend on other clustering algorithm ideas. The proposed algorithm idea is based on getting distance matrix, then the exclusion of the matrix points which will be clustered by saving the location (row, column) of these points and determine the minimum distance of these points which will be belongs the group (class) and keep the other points which are not clustering yet. The propose algorithm is applied to image data base of the human face with different environment (direction, angles... etc.). These data are collected from different resource (ORL site and real images collected from random sample of Thi_Qar city population in lraq). Our algorithm has been implemented on three types of distance to calculate the minimum distance between points (Euclidean, Correlation and Minkowski distance) .The efficiency ratio of proposed algorithm has varied according to the data base and threshold, the efficiency of our algorithm is exceeded (96%). Matlab (2014) has been used in this work.

Cite This Paper

Shaker K. Ali, Zainab Naser Azeez, Ahmed Abdul-Hussein Ouda, "A New Clustering Algorithm for Face Classification", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.6, pp.1-8, 2016. DOI:10.5815/ijitcs.2016.06.01

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International Journal of Information Technology and Computer Science (IJITCS)