Introduction - If you have any usage issues, please Google them yourself
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.