Optimized K‐Means Algorithm

Abstract

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.