In　recent　years,　with　the　continuous　development　of　the　Internet　and　artificial　intelligence,　face　recognition　technology　has　also　been　widely　used　in　many　application　scenarios.　Facing　complex　surveillance　scenarios,　face　recognition　technology　still　faces　great　challenges.　This　paper　focuses　on　implementing　real-time　and　efficient　face　recognition　systems　in　complex　surveillance　scenarios,　such　as　insufficient　lighting,　small　faces,　dense　crowds,　and　sides　at　45　environment.　The　system　is　mainly　based　on　RetinaFace　for　face　detection　and　face　alignment,　and　uses　lightweight　mobilenet　(0.25)　as　the　backbone　network　of　RetinaFace.　Facial　feature　extraction　is　based　on　deep　residual　neural　network　combined　with　ArcFace　loss,　and　feature　matching　is　performed　by　Euclidean　distance.　The　experimental　results　show　that　the　face　recognition　system　has　good　real-time　performance,　accuracy　and　robustness.　©　2019　Published　under　licence　by　IOP　Publishing　Ltd.