Increase reliability for skin detector using backprobgation neural network and heuristic rules based on YCbCr

Skin detection is a popular image processing technique that has been applied in many areas such as video-surveillance, cyber-crime prosecution and unit-spam system. It is also considered as one of the most challenging problems in image processing. Despite being a well known technique in detecting human appearance within image, it faces several drawbacks when using colour as cue to detect skin. First, the difficulty is when the colour between skin and non skin within an image is similar. Second, the skin appearance of humans when exposed under different lighting condition adds the complexity to skin detection. Therefore, this paper proposed a new hybrid module for skin detector using backprobgation neural network and heuristic rules based on YCbCr with the purpose to improve the skin detection performance. Using the new hybrid module, the researcher managed to increase the classification reliability when discriminating human skin colour and regularize the problem when exposed to different lighting conditions. The new proposed skin detector depends on normalization technique to normalize the inputs and targets so that they fall in the interval [-1, 1] and trained with training set of skin and non-skin pixels. Using the techniques discriminates human for images having upright frontal skin with any background achieved high detection rates, scored an 88.5% classification and low false positives when comparing with the previous methods.