CASIAWebface数据集

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Pushing by big data and deep convolutional neural network (CNN), the performance of face recognition is becoming comparable to human. Using private large scale training datasets, several groups achieve very high performance on LFW, i.e., 97% to 99%. While there are many open source implementations of CNN, none of large scale face dataset is publicly available. The current situation in the field of face recognition is that data is more important than algorithm. To solve this problem, we propose a semi-automatical way to collect face images from Internet and build a large scale dataset containing 10,575 subjects and 494,414 images, called CASIA-WebFace. To the best of our knowledge, the size of this dataset rank second in the literature, only smaller than the private dataset of Facebook (SCF). We encourage those data-consuming methods training on this dataset and reporting performance on LFW.

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在大数据和深度卷积神经网络&＃xff08;美国有线电视新闻网&＃xff09;的推动下&＃xff0c;人脸识别的性能已与人类相比。使用私有的大规模训练数据集&＃xff0c;若干组在LFW上实现非常高的性能&＃xff0c;即97%到99%。虽然有许多开源的美国有线电视新闻网的实现&＃xff0c;没有大规模的面部数据集是公开可用的。人脸识别领域的研究现状是数据比算法更重要。为了解决这个问题&＃xff0c;我们提出了一种半自动的方式来收集来自互联网的人脸图像&＃xff0c;并建立一个大型数据集包含10575个主题和494414个图像&＃xff0c;称为CASIA WebFACTS。据我们所知&＃xff0c;该数据集的大小在文献中排名第二&＃xff0c;仅比脸谱网&＃xff08;SCF&＃xff09;的私有数据集小。我们鼓励在这个数据集上的数据消耗方法训练和LFW上的报告性能。

大家可以到官网地址下载数据集&＃xff0c;我自己也在百度网盘分享了一份。可关注本人公众号&＃xff0c;回复“2020102001”获取下载链接。