A skin tone detection algorithm for an adaptive approach to steganography

Challenges face biometrics researchers and particularly those who are dealing with skin tone detection include choosing a colour space, generating the skin model and processing the obtained regions to fit applications. The majority of existing methods have in common the de-correlation of luminance from the considered colour channels. Luminance is underestimated since it is seen as the least contributing colour component to skin colour detection. This work questions this claim by showing that luminance can be useful in the segregation of skin and non-skin clusters. To this end, here we use a new colour space which contains error signals derived from differentiating the grayscale map and the non-red encoded grayscale version. The advantages of the approach are the reduction of space dimensionality from 3D, RGB, to 1D space advocating its unfussiness and the construction of a rapid classifier necessary for real time applications. The proposed method generates a 1D space map without prior knowledge of the host image. A comprehensive experimental test was conducted and initial results are presented. This paper also discusses an application of the method to image steganography where it is used to orient the embedding process since skin information is deemed to be psycho-visually redundant.