Face detection using skin color modeling and geometric feature

The paper proposes a robust face detection technique based on skin color clustering and geometric feature. Skin regions are extracted using Gaussian skin color model in Cb-Cr space and likelihood ratio method is used to create a binary mask. Further, morphological operations and adaptive thresholding is applied to group the potential face regions and remove noisy pixels. The region thus extracted is operated by an edge filter to find the edges inside it. Finally, best ellipse searching is used to detect the frontal face outline. The skin color model was designed using a combination of two different databases, to encompass larger skin hues. Later, a total of 165 facial images from Caltech database were randomly selected to evaluate the performance of the proposed method and an accuracy of 95% was obtained.