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Pixel-based skin detection using sinc function

机译:基于像素的皮肤检测使用SINC功能

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Skin detection is a major key in most human-related recognition systems. Parametric models have been proposed to effectively model the distribution of skin pixels. In this paper some limitations of these methods are reviewed and a new model based on the sinc function is proposed to overcome these limitations. Three different color spaces are chosen for experiments. The results are compared with the results of Gaussian, GMM and Elliptic boundary models. The method significantly outperforms Gaussian and Elliptic method and the results also show that the model slightly outperforms GMM with 2 Gaussians used. Steepest decent method was used for data fitting and COMPAQ database is used for training purposes.
机译:皮肤检测是大多数人为相关的识别系统中的主要关键。 已经提出了参数模型来有效地模拟皮肤像素的分布。 在本文中,审查了这些方法的一些局限性,并提出了一种基于SINC功能的新模型来克服这些限制。 选择三种不同的颜色空间进行实验。 将结果与高斯,GMM和椭圆边界模型的结果进行比较。 该方法显着优于高斯和椭圆形方法,结果还表明模型略微优于GMM,使用2个高斯。 最陡的体面方法用于数据拟合,并且Compaq数据库用于培训目的。

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