Face　detection　has　achieved　tremendous　strides　thanks　to　convolutional　neural　networks.　However,　dense　face　detection　remains　an　open　challenge　due　to　large　face　scale　variation,　tiny　faces,　and　serious　occlusion.　This　paper　presents　a　robust,　dense　face　detector　using　global　context　and　visual　attention　mechanisms　which　can　significantly　improve　detection　accuracy.　Specifically,　a　global　context　fusion　module　with　top-down　feedback　is　proposed　to　improve　the　ability　to　identify　tiny　faces.　Moreover,　a　visual　attention　mechanism　is　employed　to　solve　the　problem　of　occlusion.　Experimental　results　on　the　public　face　datasets　WIDER　FACE　and　FDDB　demonstrate　the　effectiveness　of　the　proposed　method.　©　2022,　Institute　of　Automation,　Chinese　Academy　of　Sciences　and　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature.