Skin detection method based on cascaded AdaBoost classifier |
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Authors: | Wan Lü Jie Huang |
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Institution: | (School of Information Science and Engineering, Southeast University, Nanjing 210096, China) |
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Abstract: | Skin detection has been considered as the principal step in many machine vision systems, such as face detection and adult
image filtering. Among all these techniques, skin color is the most welcome cue because of its robustness. However, traditional
color-based approaches poorly perform on the classification of skin-like pixels. In this paper, we propose a new skin detection
method based on the cascaded adaptive boosting (AdaBoost) classifier, which consists of minimum-risk based Bayesian classifier
and models in different color spaces such as HSV (hue-saturation-value), YCgCb (brightness-green-blue) and YCgCr (brightness-green-red).
In addition, we have constructed our own database that is larger and more suitable for training and testing on filtering adult
images than the Compaq data set. Experimental results show that our method behaves better than the state-of-the-art pixel-based
skin detection techniques on processing images with skin-like background. |
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Keywords: | |
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