  Two types of combining strategies were evaluated namely combining skin
features and combining skin classifiers. Several combining rules were applied
where the outputs of the skin classifiers are combined using binary operators
such as the AND and the OR operators, "Voting", "Sum of Weights" and a new
neural network. Three chrominance components from the YCbCr colour space that
gave the highest correct detection on their single feature MLP were selected as
the combining parameters. A major issue in designing a MLP neural network is to
determine the optimal number of hidden units given a set of training patterns.
Therefore, a "coarse to fine search" method to find the number of neurons in
the hidden layer is proposed. The strategy of combining Cb/Cr and Cr features
improved the correct detection by 3.01% compared to the best single feature MLP
given by Cb-Cr. The strategy of combining the outputs of three skin classifiers
using the "Sum of Weights" rule further improved the correct detection by 4.38%
compared to the best single feature MLP.
