Robust skin detection using multi-spectral illumination

In computer vision, many applications could greatly benefit from multi-spectral image data. Our aim is to illustrate the effectiveness of multi-spectral analysis obtained from a simple and cost-effective system. While the proposed approach is broadly applicable, in this paper we focus on the specific case of skin detection. To obtain the multi-spectral data, we have assembled a system using multiple LEDs with different spectra to illuminate the scene and a conventional RGB camera to acquire images. A methodology is proposed to avoid strict requirements on the experimental environment, by adopting a simple training procedure which is tuned for the detection of human skin. Next a specific feature set is defined and a corresponding normalization method is designed to improve the robustness to changes in skin color and incident light, issues not addressed by available prior art. Finally, we use supervised learning to train our skin detector. We demonstrate the accuracy and effectiveness of our skin detector through extensive benchmarking. The proposed methodology enables a superior performance of skin detection compared to relevant alternative proposals.