Skin Patch Detection in Real-World Images

While human skin is relatively easy to detect in controlled environments, detection in uncontrolled settings such as in consumer digital photographs is generally hard. Algorithms need to robustly deal with variations in lighting, color resolution, and imaging noise. This paper proposes a simple generative skin patch model combining shape and color information. The model is parametric and represents the spatial arrangement of skin pixels as compact elliptical regions. Its parameters are estimated by maximizing the mutual information between the model-generated skin pixel distribution and the distribution of skin color as observed in the image. The core of this work is an empirical evaluation on a database of 653 consumer digital photographs. In addition, we investigate the potential of combining our skin detector with state-of-the-art appearance-based face detectors.     «

While human skin is relatively easy to detect in controlled environments, detection in uncontrolled settings such as in consumer digital photographs is generally hard. Algorithms need to robustly deal with variations in lighting, color resolution, and imaging noise. This paper proposes a simple generative skin patch model combining shape and color information. The model is parametric and represents the spatial arrangement of skin pixels as compact elliptical regions. Its parameters are estimated...     »