The　real　visual　attention　areas　of　students　during　the　learning　process　are　vital　data.　They　represent　the　students＇　visual　concentration　and　provide　strong　support　for　the　analysis　of　learning　effects.　It　is　necessary　to　use　professional　equipment　to　collect　the　visual　attention　areas　of　students　on　a　screen　but　this　equipment　is　expensive,　so　it　cannot　be　solely　depended　upon.　A　binocular　camera　can　directly　obtain　the　depth　information　under　static　conditions;　thus,　detected　pupil　and　iris　sizes　do　not　change　as　the　user-to-screen　distance　changes.　Robust　pupil　detection　is　an　important　prerequisite　for　gaze　detection　in　a　real-world　setting.　Hence,　an　accurate　algorithm　to　calculate　the　position　of　pupils　and　the　size　of　irises　under　natural　light　is　proposed　in　this　paper.　It　is　based　on　the　positive　and　negative　oblique　projection　of　relative　linear　density.　The　MPIIGaze　dataset　was　used　to　test　the　algorithm,　and　the　experimental　results　show　that　the　algorithm　is　resistant　to　changes　in　illumination　and　the　presence　of　glasses.　Moreover,　compared　with　a　system　based　on　a　monocular　camera,　the　error　in　the　distance　between　label　points　and　gaze　location　is　decreased　by　5　pixels　and　dispersion　is　decreased　by　3　pixels.　Finally,　the　line　of　sight　is　concentrated　and　the　dispersion　is　low.　With　the　＇user-screen＇　distance　between　60cm　and　80cm,　the　accuracy　can　reach　up　to　1.5　degrees　to　2.2　degrees.