A novel hybrid module of skin detector using grouping histogram technique for Bayesian method and segment adjacent-nested technique for neural network
Skin detection is a common ancient image processing applications for detecting human images. The applications include video surveillance, naked image filters within unit-spam systems and face detection. Skin color is considered as a useful and discriminating spatial feature for many skin detection r...
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| Main Authors: | , , , , |
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| Format: | Article |
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Academic Journals
2010
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/14959/ https://academicjournals.org/journal/IJPS/article-full-text-pdf/462B7D334574 |
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| Summary: | Skin detection is a common ancient image processing applications for detecting human images. The applications include video surveillance, naked image filters within unit-spam systems and face detection. Skin color is considered as a useful and discriminating spatial feature for many skin detection related applications, but it is not robust enough to deal with complex image environments. Skin tone ranges from dark (some Africans) to light white (Caucasians and some Europeans). Other factors like light-changing conditions and the presence of objects with skin-like colors could create major difficulties in face pixel-based skin detector when color feature is used. Thus, this paper proposed a novel hybrid module using grouping histogram technique for Bayesian method and back propagation neural network with segment adjacent-nested (SAN) technique based on YCbCr and RGB color space in improving the skin detection performance. The researcher was able to increase the classification reliability in discriminating human skin color and regularizing the skin detection that is exposed to different light conditions. This novel skin detector method depends on three factors. The first part of the method involves the Bayesian part that is applied to a novel grouping histogram technique which uses 600 non-skin images in the processing and then calculates the probability density for each pixel. The second part involves applying the adjacent-nested technique in the preprocessing and calculating the probability density for each pixel in the neural part. Then a combination of the neural part and normalization technique is used to normalize the inputs and targets, so that the target falls in the interval [-1, 1] for each segment, which is created and trained with the training set of the skin and non skin segments. The third part involves a combination of the Bayesian method with the neural network segmentation methods and novel hybrid method. The study, tested on human images, has an upright frontal skin with any background. As such, the results show that the proposed system is able to achieve high detection rates of 98% segmentation and low false positives when compared with the existing methods. ©Academic Journals. |
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