Under　the　existing　technology,　due　to　the　limitation　of　some　scenes,　image　data　will　have　illumination　changes,　blurring,　occlusion,　low　resolution　and　other　issues.　These　problems　have　brought　great　challenges　to　face　detection.　At　present,　many　algorithm　models　can　recognize　face　detection　well　under　the　condition　of　positive　and　high　resolution.　However,　most　of　the　faces　in　real　scenes　are　lateral　and　have　low　resolution.　For　this　kind　of　face　detection,　the　existing　algorithm　models　will　face　the　problems　of　accuracy　and　real-time　performance.　In　this　paper,　various　models　of　face　detection　algorithms　are　deeply　studied　and　analyzed.　Combined　with　the　accuracy　and　speed　of　the　algorithm　model,　this　paper　designs　a　face　detection　algorithm　model　based　on　MTCNN　(Multi-task　Convolution　Neural　Network)　network　model.　The　algorithm　is　tested　on　the　WiderFace.　WiderFace　is　the　most　commonly　used　dataset　in　the　field　of　face　detection.　The　result　shows　that　the　algorithm　is　superior　to　other　algorithms　in　the　accuracy　and　speed　of　face　detection.　©　2019　IEEE.