Adaptive skin detection under unconstrained lighting conditions using a bigaussian model and illumination estimation

An algorithm is proposed to improve the performance of skin detection algorithms under poor illumination conditions. A hybrid skin detection model is addressed to solve these problems by combining two Gaussian models of skin under normal conditions and bright illumination. According to the distribution of the combined models, the algorithm automatically evaluates the skin segmentation result of an adaptive threshold algorithm based on a Gaussian model by estimating the illumination conditions of image. If the estimation result shows that the illumination condition is very different from the normal one, the skin color of the original image needs compensation, and then the algorithm feeds the compensated image back to the Gaussian model for finer skin detection. The experimental results show that our algorithm can cope with a complex illumination change and greatly improve skin classification performance under inferior illumination conditions.