While　great　progress　has　been　made　in　face　detection,　one　of　the　remaining　challenges　is　to　detect　the　faces　of　small　targets　in　images.　The　small　target　face　we　define,　mainly　includes　the　absolute　size　of　the　face　is　not　greater　than　32×32　pixels　and　the　relative　proportion　of　the　face　size　is　not　greater　than　one　tenth　of　the　image　size.　To　meet　this　challenge,　we　propose　an　effective　face　detector,　called　SmallFaceBoxes,　which　has　a　better　performance　in　both　speed　and　accuracy.　Specifically,　our　method　has　a　lightweight　but　powerful　network　structure　consisting　of　small　target　face　detection　layers　and　multi-scale　convolution　layers.　The　design　of　small　target　face　detection　layers　enables　detector　to　increase　the　representation　ability　of　small　and　medium-sized　faces　in　the　image　and　improve　the　detection　accuracy　of　small　faces.　The　multi-scale　convolution　layers　aim　at　enriching　the　receptive　fields　and　discretizing　anchors　over　different　layers　to　handle　faces　of　various　scales.　In　addition,　according　to　the　size　of　receptive　field,　we　propose　a　strategy　of　setting　Anchor　in　multiple　feature　layers,　thus　improving　the　recall　rate　of　small　face.　As　a　consequence,　our　algorithm　performs　well　on　the　wideface　dataset.　©　Published　under　licence　by　IOP　Publishing　Ltd.