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.
Highlights
Skin detection is used in determining pixels related to human skin
It is an important technique in image processing and the most distinctive and widely used key technology in many applications, such as face detection (Kovac et al, 2003), face tracking (Dadgostar et al, 2005), human motion analysis (Gavrila, 1999) and naked image filters (Fleck et al, 1996)
One of the major issues in using skin color in skin detection is the task of choosing a suitable color space
Summary
Skin detection is used in determining pixels related to human skin. It is an important technique in image processing and the most distinctive and widely used key technology in many applications, such as face detection (Kovac et al, 2003), face tracking (Dadgostar et al, 2005), human motion analysis (Gavrila, 1999) and naked image filters (Fleck et al, 1996). Many skin models have been developed based on colors (RGB) (Vezhnevets et al, 2003), but these approaches are not robust enough to handle different lighting conditions and complex backgrounds containing surfaces and objects with skin-like colors. Paper title Skin detection in luminance images using threshold technique Skin detection using color, texture and space information. Threshold values based skin detection Integrate color, texture and space information. Region-based methods, the features (e.g., texture) are extracted from information about a pixel and its neighbors. Skin tones range from dark (some Africans) to light white (Caucasians and some Europeans) Both the light-changing conditions (Fig. 1) and the existence of objects with skin-like colors could cause some major difficulties. To help overcome these problems, this study proposes a novel FIS for skin detection that combines both color and texture features
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