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Automatic Facial Complexion Classification Based on Mixture Model

机译:基于混合模型的肤色自动分类

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Classification of facial colors plays a vital role in Traditional Chinese Medicine (TCM), photo beautification, matching cloths and other beauty and cosmetics industry. The face color of a person is considered as a symptom to reflect the physical conditions of organs in the body. Most current methods are difficult to accurately classify the facial colors. In this paper, we propose a facial color classification method based on complexion Gaussion Mixture Model (GMM) and SVM to address this problem. Specifically, we iteratively confirm the complexion pixels belonging to the skin region based on the GMM. In the optimizing process, we extract features based on two-dimensional GMM to describe main color and minor color. Experiments are performed on our dataset with 877 face images. Experimental results demonstrate the accuracy of the proposed classification method compared with the state-of-art facial color classification method.
机译:面部颜色的分类在中医(TCM),照片美化,配套布料以及其他美容和化妆品行业中起着至关重要的作用。一个人的脸色被认为是一种反映身体器官状况的症状。当前大多数方法难以准确地对面部颜色进行分类。在本文中,我们提出了一种基于肤色高斯混合模型(GMM)和支持向量机的面部颜色分类方法来解决这个问题。具体而言,我们基于GMM反复确认属于皮肤区域的肤色像素。在优化过程中,我们基于二维GMM提取特征来描述主要颜色和次要颜色。在我们的数据集中使用877张面部图像进行了实验。实验结果表明,与最新的面部颜色分类方法相比,该分类方法的准确性更高。

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