Skin Detection using Histogram depend on the Mean Shift Algorithm
In this paper, we were introduces a skin detection
method using a histogram approximation based on the mean shift
algorithm. The proposed method applies the mean shift procedure to a
histogram of a skin map of the input image, generated by comparison
with standard skin colors in the CbCr color space, and divides the
background from the skin region by selecting the maximum value
according to brightness level. The proposed method detects the skin
region using the mean shift procedure to determine a maximum value
that becomes the dividing point, rather than using a manually selected
threshold value, as in existing techniques. Even when skin color is
contaminated by illumination, the procedure can accurately segment
the skin region and the background region. The proposed method may
be useful in detecting facial regions as a pretreatment for face
recognition in various types of illumination.
[1] Z. Jiang, Z. Wu and M. Yao. "Skin Detection on Images with Color
Deviation", IEEE Trans Congress on Services, Part Ôàí, pp. 171-174, 2008.
[2] S. Kherchaoui and A. Houacine, "Face Detection Based on A Model of
the Skin Color with Constranins and Template Matching", Int'l Conf.
Machine and Web Intell. pp. 469-472., 2010
[3] L. Zhengming, Z. Tong and Z. Jin," Skin Detection in Color Images", Int'l
Conf. ICCET, pp. 156-159, 2010.
[4] T. Uongqiu, Y. Faling, C. Guohua and J. Shizhong." Skin Color
Detection by Illumination Estimation and Normalization in Shadow
Regions", IEEE. Conf. ICIA. pp. 1082-1085, 2010.
[5] D.A. Socolinsky, A. Selinger, and J.D. Neuheisel," Face Recognition
with Visible and Thermal Infrared Imagery", Computer Vision Image
Understanding, vol. 91, no.2, pp. 72-114, 2003.
[6] S.G. Kong, J. Heo, B.R. Abidi, J. Paik, and M.A. Abidi, "Recent
Advances in Visual and Infrared Face Recognition: A Review".
Computer Vision Image Understanding, vol.97, no.1, pp.103-135, 2005.
[7] A.S. Nunez and M. J Mendenhall," Detection of Human Skin in Near
Infrared Hyperspectral Imagery", IEEE. Int'l IGARSS. 2, pp. 621-624,
2008.
[8] C. Liensberger, J. Stottinger and M. Kampel, "Color-Based and
Context-Aware Skin Detection for Online Video Annotation", IEEE.
Trans. Intl'l MMSP. pp. 1-6, 2009.
[9] Z. Pan, G. Healey, M. Prasad, and B. Tromberg, "Face Recognition in
Hyperspectral Images", IEEE Trans. Pattern Anal. Mach. Intell, vol.25,
no.12, pp.1552-1559, 2003.
[10] W. Xinyu, X, Huosheng, W. Heng and L. Heng, "Robust Real-Time Face
Detection with Skin Color Detection and The Modified Census
Transform". Int'l Conf. ICIA. pp. 590-595, 2008.
[11] R. L. Hsu, M. Abdel-Mottaleb, and A. K. Jain. "Face Detection in Color
Images", IEEE Trans. on PAMI, vol.24, no.5, pp.696-706, 2002.
[1] Z. Jiang, Z. Wu and M. Yao. "Skin Detection on Images with Color
Deviation", IEEE Trans Congress on Services, Part Ôàí, pp. 171-174, 2008.
[2] S. Kherchaoui and A. Houacine, "Face Detection Based on A Model of
the Skin Color with Constranins and Template Matching", Int'l Conf.
Machine and Web Intell. pp. 469-472., 2010
[3] L. Zhengming, Z. Tong and Z. Jin," Skin Detection in Color Images", Int'l
Conf. ICCET, pp. 156-159, 2010.
[4] T. Uongqiu, Y. Faling, C. Guohua and J. Shizhong." Skin Color
Detection by Illumination Estimation and Normalization in Shadow
Regions", IEEE. Conf. ICIA. pp. 1082-1085, 2010.
[5] D.A. Socolinsky, A. Selinger, and J.D. Neuheisel," Face Recognition
with Visible and Thermal Infrared Imagery", Computer Vision Image
Understanding, vol. 91, no.2, pp. 72-114, 2003.
[6] S.G. Kong, J. Heo, B.R. Abidi, J. Paik, and M.A. Abidi, "Recent
Advances in Visual and Infrared Face Recognition: A Review".
Computer Vision Image Understanding, vol.97, no.1, pp.103-135, 2005.
[7] A.S. Nunez and M. J Mendenhall," Detection of Human Skin in Near
Infrared Hyperspectral Imagery", IEEE. Int'l IGARSS. 2, pp. 621-624,
2008.
[8] C. Liensberger, J. Stottinger and M. Kampel, "Color-Based and
Context-Aware Skin Detection for Online Video Annotation", IEEE.
Trans. Intl'l MMSP. pp. 1-6, 2009.
[9] Z. Pan, G. Healey, M. Prasad, and B. Tromberg, "Face Recognition in
Hyperspectral Images", IEEE Trans. Pattern Anal. Mach. Intell, vol.25,
no.12, pp.1552-1559, 2003.
[10] W. Xinyu, X, Huosheng, W. Heng and L. Heng, "Robust Real-Time Face
Detection with Skin Color Detection and The Modified Census
Transform". Int'l Conf. ICIA. pp. 590-595, 2008.
[11] R. L. Hsu, M. Abdel-Mottaleb, and A. K. Jain. "Face Detection in Color
Images", IEEE Trans. on PAMI, vol.24, no.5, pp.696-706, 2002.
@article{"International Journal of Electrical, Electronic and Communication Sciences:51314", author = "Soo- Young Ye and Ki-Gon Nam and Ki-Won Byun", title = "Skin Detection using Histogram depend on the Mean Shift Algorithm", abstract = "In this paper, we were introduces a skin detection
method using a histogram approximation based on the mean shift
algorithm. The proposed method applies the mean shift procedure to a
histogram of a skin map of the input image, generated by comparison
with standard skin colors in the CbCr color space, and divides the
background from the skin region by selecting the maximum value
according to brightness level. The proposed method detects the skin
region using the mean shift procedure to determine a maximum value
that becomes the dividing point, rather than using a manually selected
threshold value, as in existing techniques. Even when skin color is
contaminated by illumination, the procedure can accurately segment
the skin region and the background region. The proposed method may
be useful in detecting facial regions as a pretreatment for face
recognition in various types of illumination.", keywords = "Skin region detection, mean shift, histogram
approximation.", volume = "6", number = "7", pages = "618-3", }