Increasing the Reliability of Fuzzy Inference System-Based Skin Detector

Abstract

Problem statement: Skin detection is a common primitive for many human-related image processing applications, such as video surveillance, naked image filters and face detection. Skin color is considered as a useful and discriminating spatial feature for many applications, but it is not robust enough to deal with complex image environments. Skin tones range from dark (some Africans) to light white (Caucasians and some Europeans). In addition, both the light-changing conditions and the existence of objects with skin-like colors could cause some major difficulties faced pixel-based skin detector depending only on a color feature. Approach: This study proposed a novel Fuzzy Inference System (FIS) for skin detection, which combines both color and texture features. To increase the reliability of the skin detection process, neighborhood pixel information is incorporated into the proposed method. The color feature is represented using RGB color model, while the texture feature is estimated using three statistical measures: standard deviation, entropy and range. The subtractive clustering-based fuzzy system method and the Sugeno type reasoning mechanism are used for modeling FIS-based skin detection. The proposed approach builds a fuzzy model of skin detection from existing images within skin and non-skin regions (output data) and from both color and texture features of the skin regions (input data). Results: The proposed skin detection method achieved a true positive rate of approximately 90% and a false positive rate of approximately 0.22%. Furthermore, this study analyzes and compares the obtained results from the proposed skin detection with threshold-based skin detector to show the level of robustness, using both color and texture features in the proposed skin detector. Conclusion: It was found that a skin detector based on both color and texture features can lead to an efficient and more reliable skin detection method compared with other state-of-the-art threshold-based skin detectors. The proposed detector reduces the FP rate to 0.22% compared with a skin detector based on predefined color rules.