Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of skin-color modeling and detection methods
Pattern Recognition
Naked image detection based on adaptive and extensible skin color model
Pattern Recognition
Saliency model-based face segmentation and tracking in head-and-shoulder video sequences
Journal of Visual Communication and Image Representation
A skin detection approach based on color distance map
EURASIP Journal on Advances in Signal Processing
Detecting skin in face recognition systems: A colour spaces study
Digital Signal Processing
Skin detection using neighborhood information
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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This paper presents a reliable color pixel clustering model for skin segmentation under unconstrained scene conditions. The proposed model can overcome sensitivity to variations in lighting conditions and complex backgrounds. Our approach is based on building multi-skin color clustering models using the Hue, Saturation, and Value color space and multi-level segmentation. Skin regions are extracted using four skin color clustering models, namely, the standard-skin, shadow-skin, light-skin, and high-red-skin models. Moreover, skin color correction (skin lighting) at the shadow-skin layer is used to improve the detection rate. The experimental results from a large image data set demonstrate that the proposed clustering models could achieve a true positive rate of 96.5% and a false positive rate of approximately 0.765%. The experimental results show that the color pixel clustering model is more efficient than other approaches.