Skin color constancy for illumination invariant skin segmentation

Accuracy of skin segmentation algorithms is highly sensitive to changes in lighting conditions. When the lighting condition in a scene is different from that in the training examples, miss-classification rate of the skin segmentation algorithms is high. Using color constancy approach we aim to compensate for skin color variations to achieve accurate skin color segmentation. Skin color constancy is realized in an unsupervised manner by using the color changes observed on face regions under different illuminations to drive the model. By training on a few faces of different ethnicities, our model is able to generalize the color mapping for any unseen ethnicity. The color changes observed are used to learn the color mapping from one lighting condition to the other. We show the proof of concept of unsupervised skin color constancy on faces from the PIE database. Skin segmentation with and without color compensation was performed on the PIE database. Results are presented which show improved skin segmentation accuracy after performing color compensation.