Face　detection　tasks　under　the　current　epidemic　prevention　situation　often　acquire　images　with　partial　occlusion.　General　face　detectors　ignore　the　challenge　brought　by　occlusion,　making　it　difficult　to　meet　daily　needs.　In　order　to　address　this　problem,　this　paper　proposes　a　real-time　occluded　face　detection　network　based　on　the　improved　CenterNet　with　information　dropping　strategy.　First,　depth　separable　convolution　and　attention　mechanism　are　introduced　into　the　backbone　to　reduce　parameters　and　extract　occlusion-robust　features.　Second,　a　feature　fusion　neck　is　designed　to　improve　the　performance　of　multi-scale　face　detection.　In　addition,　the　data　augmentation　method　with　information　removal　strategy　enriches　the　diversity　of　occlusion　samples.　Experiments　indicate　that　our　model　improves　the　fps　as　well　as　maintains　the　accuracy.　©　2022　IEEE.