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.
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