Multi-scale Fully Convolutional Network for Face Detection in the Wild

Yancheng Bai, Bernard Ghanem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Face detection is a classical problem in computer vision. It is still a difficult task due to many nuisances that naturally occur in the wild. In this paper, we propose a multi-scale fully convolutional network for face detection. To reduce computation, the intermediate convolutional feature maps (conv) are shared by every scale model. We up-sample and down-sample the final conv map to approximate K levels of a feature pyramid, leading to a wide range of face scales that can be detected. At each feature pyramid level, a FCN is trained end-to-end to deal with faces in a small range of scale change. Because of the up-sampling, our method can detect very small faces (10×10 pixels). We test our MS-FCN detector on four public face detection datasets, including FDDB, WIDER FACE, AFW and PASCAL FACE. Extensive experiments show that it outperforms state-of-the-art methods. Also, MS-FCN runs at 23 FPS on a GPU for images of size 640×480 with no assumption on the minimum detectable face size.
Original languageEnglish (US)
Title of host publication2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2078-2087
Number of pages10
ISBN (Print)9781538607336
DOIs
StatePublished - Aug 24 2017

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