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DeepEthnic: Multi-label Ethnic Classification from Face Images

机译:深刻的:面部图像的多标签种族分类

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Ethnic group classification is a well-researched problem, which has been pursued mainly during the past two decades via traditional approaches of image processing and machine learning. In this paper, we propose a method of classifying an image face into an ethnic group by applying transfer learning from a previously trained classification network for large-scale data recognition. Our proposed method yields state-of-the-art success rates of 99.02%, 99.76%, 99.2%, and 96.7%, respectively, for the four ethnic groups: African, Asian, Caucasian, and Indian.
机译:族群分类是一项良好的问题,主要是通过传统的图像处理和机器学习方法在过去二十年中追求。在本文中,我们提出了一种通过从先前培训的分类网络应用转移学习进行大规模数据识别,提出将图像面部分类为族群的方法。我们的拟议方法分别为四个族裔群体产生99.02%,99.76%,99.2%和96.7%的最先进的成功率:非洲,亚洲,白种人和印度人。

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