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Machine Learning Based Approach for Person Identification in Group Photos

机译:基于机器学习的群体识别方法

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In this digital era, capturing photos using smartphone camera is very handy. Especially when we are hanging out with friends or family or attending a wedding and so on, we end up taking many group photos. But when browsing through these group photos, most of the times, a person is interested in only those photos in which he himself is present. So, currently we manually browse through such group photos in the phone gallery and then identify the pictures in which the specific person is present. For group photos, this procedure needs to be repeated for every person in the group. In this paper, we have designed and implemented an Android platform based photo grouping application named "EuphoriaGrouping" (EUG) using Neural Networks. EUG application automates the process of detecting faces and identifying persons from a group photo. It maintains a catalogue of group photos for every person present in the group photo. For person identification, two different convolutional neural network models, viz, Custom Built and OpenFace CNN are used. Implementation and performance comparison of these models is presented.
机译:在这个数字时代,使用智能手机相机捕获照片非常方便。特别是当我们和朋友或家人一起出去或参加婚礼等时,我们最终拍了很多组照片。但是,在浏览这些群体照片时,大部分时间都是一个人只对他自己所在的照片感兴趣。因此,目前我们手动浏览电话库中的这些组照片,然后识别所在的图片。对于组照片,需要为本集团中的每个人重复此过程。在本文中,我们使用神经网络设计并实现了名为“EuphoriaGrouping”(eug)的基于Android平台的照片分组应用程序。 eug应用程序自动化检测面部和识别组照片的过程。它为集团照片中的每个人维护了一张小组照片的目录。对于人身份证明,使用了两个不同的卷积神经网络模型,VIZ,定制构建和Openface CNN。提出了这些模型的实现和性能比较。

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