Detecção de pele humana em imagens veiculadas na web

Abstract

Face detection, gesture recognition and pornography content assessment are some of the applications that require the detection of human skin in digital imagery. Most methods employ color as the main feature for this task. Whenever the acquisition conditions are controlled, there is available information about illumination, resolution and geometry, making the skin detection problem a relatively easy task for which there are plenty of results in the literature. The problem becomes more challenging in less structured conditions, mainly because of the influence illumination conditions have on the apparent color of objects. There are proposals for color correction that lead to both good and bad classification results, depending on the input data. When dealing with Web imagery, little can be assumed about their content or about the conditions in which they were acquired, and robust techniques are needed for skin detection. This MSc thesis makes a qualitative assessment of seven skin detection models and of four different types of input data. A heuristic is proposed for deciding if an image requires color correction and, if needed, which is the best suited technique. Results are compared by means of measures derived from confusion matrices, and our approach produces competitive classification products.