Skin Color Detection In Static Images

Skin detection plays a key role in many computer vision tasks like face detection and recognition, expression recognition, gestures recognition, human detection, content-based image and video indexing, etc, especially in adult image detection. It has been widely used in the area such as human machine interface, access control, surveillance and objectionable Web images filtering system and so on. Skin color has proven to be a powerful cue for skin detection in images because of its advantages: low computational cost and robustness against viewpoint changing and geometrical transformations. This dissertation focuses on detection of skin color in static images.The choice of the color space and the way of modeling the skin color distribution are key problems for skin color detection. We review the commonly used color spaces and skin color models comprehensively, and classify them according their properties. Using a large data set of 1894 images, we examine whether the color space transformation can increase the compactness of skin class and the discriminability between skin and non-skin classes in seventeen color spaces. We also evaluate the classification performance of SPM, GMM, SOM and SVM in these color spaces. The effect of histogram size, dropping illumination, precision of model and decision strategy is analyzed respectively. This new comprehensive color space and color model testing methodology would allow for making the best choices for skin detection in general.The SOM changes only gradually during its final fine-tuning phase, the new winner of same training input may be found at or in the vicinity of the old one. We propose a novel local search strategy based on movement of surroundings weight vector to accelerate winner search. Experiments with artificial and real world data showed that the local search algorithm is noticeably better in performance than the conventional one.Due to variations of lighting conditions, camera hardware settings, and the range of skin coloration among human beings, a predefined skin color model cannot accurately capture the wide distribution of skin colors in individual images. The most common used illuminant compensation and adaptation approach, dynamic model including dynamic histogram and dynamic Gaussian model are reviewed in detail in chapter 4.The human is usually well focused when captured by the lens system, whereas background objects are typically blurred to out-of-focus. It is difficult to find a closed boundary for focused skin regions. We propose a skin color region detection solution is based on salient boundary segment. A skin-like region containing or adjoining salient boundary segment is regard as skin region. Those defocused skin-like regions...