Fuzzy skin detection

The robustness of web technologies allows huge collection of information to be available through internet. Among this information, some of them are unhealthy and undesirable. This kind of harmful information will make bad impact on human development especially to children and teenagers. Therefore, an explicit image filtering method based on skin detection is proposed in this project. Skin detection is a popular image processing technique that has been applied in many areas such as video-surveillance, cyber-crime prosecution and face detection. It is well known technique to detect the human appearance within image. However, it faces several drawbacks when using color as cue to detect skin. First, the similarity between skin and background color within an image. Second, the skin appearance of human under different lightning condition also adds the complexity in skin detection. Third, different camera characteristics also influence the performance of skin detection. Therefore in this project, fuzzy theory is proposed to improve the skin detection performance by solving the first problem. By improving the first problem, we can increase the classification accuracy when discriminate human and animal skin images which was tested in this project. The complexity of applying fuzzy theory in skin detection is based on the highly similarity between skin and non-skin pixels as a clear description of the skin color set is needed for processes. Hence, the rules need to be explicitly enough in order to achieve better performances. Finally, experiment has been conducted to test the applicability of fuzzy classification. Although the classification result is lower than comparison method, fuzzy theory has been proved to be able to discriminate human and animal skin.