This paper presents a new human skin color model in

YCbCr color space and its application to human face

detection. Skin colors are modeled by a set of three

Gaussian clusters, each of which is characterized by a

centroid and a covariance matrix. The centroids and

covariance matrices are estimated from large set of

training samples after a k-means clustering process. Pixels

in a color input image can be classified into skin or nonskin

based on the Mahalanobis distances to the three

clusters. Efficient post-processing techniques namely noise

removal, shape criteria, elliptic curve fitting and faceinonface

classification are proposed in order to !inther refine

skin segmentation results for the purpose of face detection.