Frontal-view Face Detection in The Presence of Skin-Tone Regions Using a New Symmetry Approach

Abstract

In this paper, an efficient algorithm for detecting frontalview faces in color images is proposed. The proposed algorithm has a special task; it detects faces in the presence of skin-tone regions such as human body, clothes, and background. Firstly, a pixel based color classifier is applied to segment the skin pixels from background. Next, a hybrid cluster algorithm is applied to partition the skin region. It is well known that the frontal face is symmetrical; therefore we introduce a new symmetry approach, which is the main distinguishing feature of the proposed algorithm. It measures a symmetrical value, searches for the real center of the region, and then removes the extra unsymmetrical skin pixels. The cost functions are adopted to locate the real two eyes of the candidate face region. Finally, a template matching process is preformed between an aligning frontal face model and the candidate face region as a verification step. We have tested our algorithm on 200 images from different sets. Experimental results reveal that our algorithm can perform the detection of faces successfully under wide variations of captured images.