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Dual-bound-constraints in morphological model refinement for multiple skin color tones detection

机译:双重约束的形态模型优化中的多种肤色检测

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This paper aims to propose a solution to skin color detection problem when existing multiple skin-color target persons in the image. The AdaBoost algorithm is used to detect the position of the face, and the dominant color of every face regions is used as the initial skin color sample. A new type of sample update mechanism is used to increase the skin color detection accuracy rate, with a constraint mechanism controlling the number of iterations, allowing the proposed system to find out a more complete and smooth skin color regions. In comparison to other methods with fixed samples, the skin color samples in this study varies with each execution, and the resistance to the skin color shift caused by light and shade is improved.
机译:本文旨在为图像中存在多个肤色目标人群时的肤色检测问题提出一种解决方案。 AdaBoost算法用于检测脸部位置,每个脸部区域的主色用作初始皮肤颜色样本。一种新型的样本更新机制用于提高皮肤颜色检测的准确率,并使用约束机制控制迭代次数,从而使所提出的系统能够找到更完整,更平滑的皮肤颜色区域。与使用固定样本的其他方法相比,本研究中的皮肤颜色样本每次执行都会有所不同,并且改善了对由明暗引起的皮肤颜色偏移的抵抗力。

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