文件名称:A-NOVEL-SKIN-YCBCR-COLOR-SPACE
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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.
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.
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A NOVEL SKIN COLOR MODEL IN YCBCR COLOR SPACE.pdf