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Person detection in hyperspectral images via skin segmentation using an active learning approach

Human skin detection is a computer vision problem that has been widely researched in color images. In this article we deal with this task as an interactive segmentation problem in hyperspectral outdoor images. We have focused on the problem of skin identification in hyperspectral cameras allowing a fine sampling of the light spectrum, so that the information gathered at each pixel is a high dimensional vector. The problem is treated as a classification problem, where we make use of active learning strategies to provide an interactive robust solution reaching high accuracy in a short training/testing cycle.