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Mathematical Problems in Engineering
Volume 2014, Article ID 506480, 14 pages
Research Article

Optimized K-Means Algorithm

1Department of Mathematics & Computer Science, College of Science and General Studies, Alfaisal University, P.O. Box 50927, Riyadh, Saudi Arabia
2Department of Software Engineering, College of Engineering, Alfaisal University, P.O. Box 50927, Riyadh, Saudi Arabia

Received 18 May 2014; Revised 9 August 2014; Accepted 13 August 2014; Published 7 September 2014

Academic Editor: Gradimir Milovanović

Copyright © 2014 Samir Brahim Belhaouari et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The localization of the region of interest (ROI), which contains the face, is the first step in any automatic recognition system, which is a special case of the face detection. However, face localization from input image is a challenging task due to possible variations in location, scale, pose, occlusion, illumination, facial expressions, and clutter background. In this paper we introduce a new optimized k-means algorithm that finds the optimal centers for each cluster which corresponds to the global minimum of the k-means cluster. This method was tested to locate the faces in the input image based on image segmentation. It separates the input image into two classes: faces and nonfaces. To evaluate the proposed algorithm, MIT-CBCL, BioID, and Caltech datasets are used. The results show significant localization accuracy.