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A NEW SYMMETRY APPROACH FOR FRONTAL-VIEW FACE DETECTION

El_Sayed M. Saad, Mohiy M. Hadhoud, Moawad I. Moawad, Mohamed El_Halawany, Alaa M. Abbas

Abstract


An efficient algorithm for detecting frontal-view faces in color images is proposed. The proposed algorithm has a special task; it detects faces in the presence of skin-tone regions (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. 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. A template matching process is preformed between an aligning frontal face model and the candidate face region as a verification step. Experimental results reveal that our algorithm can perform the detection of faces successfully under wide variations.

Keywords


Face detection; image segmentation; clustering; cost functions; symmetry approach

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References


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