Skin classification based on neural networks seems to be a promising method. 2. Parametric skin modeling methods are better suited for constructing classifiers in case of limited training and expected target data set. The generalization and interpolation ability of these methods makes it possible to construct a classifier with acceptable performance from incomplete training data. 3. Excluding color luminance from the classification process cannot help achieving better discrimination of skin and non-skin colors, but can help to generalize sparse training data. . Evaluation of color space goodness ’in general’ by assessing skin/non-skin overlap, skin cluster shape, etc. regardless to any specific skin modeling method cannot give the impression of how good is the color space suited for skin modeling, because different modeling methods react very differently on the color space change. 5. Color distributions for skin and non-skin pixel classes learned from web images can be used to fashion a surprisingly accurate pixel-wise skin detector with an equal error rate of 88%.
The key is the use of a very large labeled dataset to capture the effects of the unconstrained imaging environment represented by web photos. One possible advantage of using a large dataset is that simple learning rules, such as histogram density estimators, may give good performance. This can result in computationally simple algorithms for learning and classification. We show that in our context histogram classifiers compare favorably to the more expensive but widely-used Gaussian mixture densities.
The Essay on Colorism: Black People and Skin Color
Growing up as a youth being in an interracial family, I always experienced prejudice whether it was inside my home or out on the street. My father was an African-American, his family was accepting but all could see that they praised the fact that my skin was 5-6 shades lighter than that of my other cousins. This of course caused unresolved issues, issues that couldn’t and wouldn’t be talked about ...
