Color space selection for human skin detection using color-texture features and neural networks

Skin color is a robust cue in human skin detection. It has been widely used in various human-related image processing applications. Although many researches have been carried out for skin color detection, there is no consensus on which color space is the most appropriate for skin color detection because many researchers do not provide strict justification of their color space choice. In this paper, a comprehensive comparative study using the Multilayer Perceptron artificial neural network (MLP), which is a universal classifier, is carried out to evaluate the overall performance of different color-spaces for skin detection. It aims at determining the most optimal color space using color and color-texture features separately. The study has been carried out using images of different databases. The experimental results showed that the YIQ color space gives the highest separability between skin and non-skin pixels among the different color spaces tested using color features. Combining color and texture eliminates the differences between color spaces but leads to much more accurate and efficient skin detection.