A new hybrid module for skin detector using fuzzy inference system structure and explicit rules - Zaidan, A.A.; Karim, H.A.; Ahmad, N.N.; Alam, G.M.; Zaidan, B.B. (Academic Journals, 2010)
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 also considered as one of the challenging problems in image processing. Despite being a well known technique to detect human appearance within image, it faces a fundamental problem when using colour as cue to detect skin. It is difficult to detect skin when the colour between the skin and the non skin within an image is similar. Therefore in this paper, a new hybrid module between explicit rules and fuzzy inference system structure, based on RGB colour space, is proposed to improve skin detection performance. Using the new hybrid module, we managed to increase the classification reliability when discriminating human skin. The new proposed skin detector depends on subtractive clustering technique, created and trained with training set of skin and non-skin pixels. The proposed system is tested on human images having upright frontal skin with any background. Our proposed system has achieved high detection rates of 87% classification and low false positives when compared with the existing methods.