A Survey on Pixel-Based Skin Color Detection Techniques

By Vladimir Vezhnevets, V. Sazonov, A. Andreeva. 2003

1,244 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c4534c59bb5d3fc8cd11b0f9f371e39e9b9fcc2b

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Citations in the corpus

A survey of skin-color modeling and detection methods (2007, methodology): “A survey of different color spaces for skin-color representation and skin-pixel detection methods is given by Vezhnevets et al. [10].”
Demographic Effects in Facial Recognition and Their Dependence on Image Acquisition: An Evaluation of Eleven Commercial Systems (2019, methodology): “This method is distinct from using non-linear color space transformations like CIELab color or YCbCr that have been previously examined in their utility for segmenting skin in images [14].”
Depth-Based Hand Pose Estimation: Methods, Data, and Challenges (2015, unknown): “Typically, the depth image is segmented with simple morphological operations [23] or the RGB image is segmented with skin classifiers [40].”
Urban tribes: Analyzing group photos from a social perspective (2012, background): “It comprises five different descriptors, f low = [skin r , rgb h , hue h , HoG, textons], for which skin r is the ratio of detected skin pixels [14] relative to part size, rgb h is a color histogram of 8 bins per color band, hue h a histogram of 16 bins for hue values, HoG are the features proposed…”
A Physical Model of Human Skin and its Application for Search and Rescue (2012, background): “The disadvantages of the RGB color space is the strong correlation between the channels [92].”
Two streams Recurrent Neural Networks for Large-Scale Continuous Gesture Recognition (2016, background): “are usually detected by their color [21] or multiple proposals [22].”