  Skin colour detection is frequently been used for searching people, face
detection, pornographic filtering and hand tracking. The presence of skin or
non-skin in digital image can be determined by manipulating pixels colour or
pixels texture. The main problem in skin colour detection is to represent the
skin colour distribution model that is invariant or least sensitive to changes
in illumination condition. Another problem comes from the fact that many
objects in the real world may possess almost similar skin-tone colour such as
wood, leather, skin-coloured clothing, hair and sand. Moreover, skin colour is
different between races and can be different from a person to another, even
with people of the same ethnicity. Finally, skin colour will appear a little
different when different types of camera are used to capture the object or
scene. The objective in this study is to develop a skin colour classifier based
on pixel-based using RGB ratio model. The RGB ratio model is a newly proposed
method that belongs under the category of an explicitly defined skin region
model. This skin classifier was tested with SIdb dataset and two benchmark
datasets; UChile and TDSD datasets to measure classifier performance. The
performance of skin classifier was measured based on true positive (TF) and
false positive (FP) indicator. This newly proposed model was compared with
Kovac, Saleh and Swift models. The experimental results showed that the RGB
ratio model outperformed all the other models in term of detection rate. The
RGB ratio model is able to reduce FP detection that caused by reddish objects
colour as well as be able to detect darkened skin and skin covered by shadow.
