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Automatic Age Classification of Prospective Voters Using Deep Convolutional Neural Network.

机译:使用深度卷积神经网络自动对准选民进行年龄分类。

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Age estimation from images of the face, has gained more attention in recent as it is favorable in some realworld applications. In this work, we address the problem of Under-Age registration/voting in Nigeria Electoral system, which has been a major menace, hindering a free and fair election in the country since the assumption of the Democratic system of Government. To this end, a pretrained VGG-16 Deep Convolutional Neural Network while comparing two optimization algorithms without image preprocessing is employed to both extract features from image(s) of prospective voters and classify same under the established age classification group, as eligible or not to exercise their civil right. In light of this, a classification accuracy of 77.67% is achieved with the model.
机译:根据脸部图像进行年龄估计,由于它在某些实际应用中是有利的,因此最近受到了越来越多的关注。在这项工作中,我们解决了尼日利亚选举制度中的未成年登记/投票问题,这一直是一个主要威胁,阻碍了自从实行民主政府制度以来该国的自由公正选举。为此,采用预训练的VGG-16深层卷积神经网络,同时比较两种不使用图像预处理的优化算法,以从预期选民的图像中提取特征并将其分类为既定年龄分类组(是否符合条件)行使其公民权利。有鉴于此,该模型的分类精度达到了77.67%。

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