Nowadays,　face　detection　and　head　pose　estimation　have　a　lot　of　application　such　as　face　recognition,　aiding　in　gaze　estimation　and　modeling　attention.　For　these　two　tasks,　it　is　usually　to　design　two　different　models.　However,　the　head　pose　estimation　model　often　depends　on　the　region　of　interest　(ROI)　detected　in　advance,　which　means　that　a　serial　face　detector　is　needed.　Even　the　lightest　face　detector　will　slow　down　the　whole　forward　inference　time　and　cannot　achieve　real-time　performance　when　detecting　the　head　pose　of　multiple　people.　We　can　see　that　both　face　detection　and　head　pose　estimation　need　face　features,　so　a　shared　face　feature　map　can　be　used　between　them.　In　this　paper,　a　multi-task　learning　model　is　proposed　that　can　solve　both　problems　simultaneously.　We　directly　detect　the　location　of　the　center　point　of　the　bounding　box　of　face;　at　this　location,　we　calculate　the　size　of　the　bounding　box　of　face　and　the　head　attitude.　We　evaluate　our　model＇s　performance　on　the　AFLW.　The　proposed　model　has　great　competitiveness　with　the　multi-stage　face　attribute　analysis　model,　and　our　model　can　achieve　real-time　performance.