CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术研究
Learning Face Representation from Scratch
Yi D(易东); Lei Z(雷震); Liao SC(廖胜才); Li ZQ(李子青)
Source PublicationarXiv preprint
AbstractPushing 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, ie, 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, this paper proposes a semi-
automatical way to collect face images from Internet and builds a large scale dataset
containing about 10,000 subjects and 500,000 images, called CASIAWebFace. Based on
the database, we use a 11-layer CNN to learn discriminative representation and obtain state-
of-theart accuracy on LFW and YTF.
Document Type期刊论文
Recommended Citation
GB/T 7714
Yi D,Lei Z,Liao SC,et al. Learning Face Representation from Scratch[J]. arXiv preprint,2014(11):1.
APA Yi D,Lei Z,Liao SC,&Li ZQ.(2014).Learning Face Representation from Scratch.arXiv preprint(11),1.
MLA Yi D,et al."Learning Face Representation from Scratch".arXiv preprint .11(2014):1.
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