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dc.contributor.authorBai, Yancheng
dc.contributor.authorGhanem, Bernard
dc.date.accessioned2017-12-28T07:32:14Z
dc.date.available2017-12-28T07:32:14Z
dc.date.issued2017-07-20
dc.identifier.urihttp://hdl.handle.net/10754/626520
dc.description.abstractFace detection is a fundamental problem in computer vision. It is still a challenging task in unconstrained conditions due to significant variations in scale, pose, expressions, and occlusion. In this paper, we propose a multi-branch fully convolutional network (MB-FCN) for face detection, which considers both efficiency and effectiveness in the design process. Our MB-FCN detector can deal with faces at all scale ranges with only a single pass through the backbone network. As such, our MB-FCN model saves computation and thus is more efficient, compared to previous methods that make multiple passes. For each branch, the specific skip connections of the convolutional feature maps at different layers are exploited to represent faces in specific scale ranges. Specifically, small faces can be represented with both shallow fine-grained and deep powerful coarse features. With this representation, superior improvement in performance is registered for the task of detecting small faces. We test our MB-FCN detector on two public face detection benchmarks, including FDDB and WIDER FACE. Extensive experiments show that our detector outperforms state-of-the-art methods on all these datasets in general and by a substantial margin on the most challenging among them (e.g. WIDER FACE Hard subset). Also, MB-FCN runs at 15 FPS on a GPU for images of size 640 x 480 with no assumption on the minimum detectable face size.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1707.06330v1
dc.relation.urlhttp://arxiv.org/pdf/1707.06330v1
dc.rightsArchived with thanks to arXiv
dc.titleMulti-Branch Fully Convolutional Network for Face Detection
dc.typePreprint
dc.contributor.departmentVisual Computing Center (VCC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.eprint.versionPre-print
dc.identifier.arxivid1707.06330
kaust.personBai, Yancheng
kaust.personGhanem, Bernard
dc.versionv1
refterms.dateFOA2018-06-13T10:26:16Z


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