Human skin detection in the visible and near infrared

Skin detection is a well-studied area in color imagery and is useful in a number of scenarios to include search and rescue and computer vision. Most approaches focus on color imagery due to cost and availability. Many of the visible-based approaches do well at detecting skin (above 90%) but they tend to have relatively high false-alarm rates (8%-15%). This article presents a novel feature space for skin detection in visible and near infrared portions of the electromagnetic spectrum. The features are derived from known spectral absorption of skin constituents to include hemoglobin, melanin, and water as well as scattering properties of the dermis. Fitting a Gaussian mixture to skin and background distributions and using a likelihood ratio test detector, the features presented here show dominating performance when comparing receiver-operating characteristic curves (ROCs) and statistically significant improvement when comparing equal error rates and area under the ROC (AUC). A detection/false-alarm probability of 98.6%/1.1% is achieved for the averaged equal error rate (EER). EER values for the proposed feature space show a 5.6%-11.2% increase in detection probability with a 6.0%-11.6% decrease in false-alarm probability compared to well performing color-based features. The AUC shows a 0.034-0.173 increase in total area under the curve compared to well performing color-based features.