Research　shows　that　physiological　signals　can　provide　objective　data　support　for　the　analysis　of　human　emotions.　At　present,　non-contact　heart　rate　data　have　been　employed　in　the　research　of　medicine,　intelligent　transportation,　smart　education,　etc.　However,　it　is　hard　to　detect　heart　rate　data　using　non-contact　traditional　methods　during　head　rotation,　especially　when　face　information　is　missing　in　scenarios　such　as　online　teaching/learning.　Traditional　remote　photoplethysmography　(rPPG)　methods　require　a　static,　full　frontal　face　within　a　fixed　distance　for　heart　rate　detection.　These　strict　requirements　make　it　impractical　to　measure　heart　rate　data　in　real-world　scenarios,　as　a　lot　of　videos　only　partially　record　the　subjects＇　face　information,　such　as　profile,　too　small　distance,　and　wearing　a　mask.　The　current　algorithm　aims　to　solve　the　problem　of　head　deflections　between　30　degrees　and　45　degrees　by　employing　a　symmetry　substitution　method,　which　can　replace　the　undetected　region　of　interest　(ROI)　with　the　detectable　one.　When　face　information　is　partially　missing,　our　algorithm　uses　face-eye　location　to　determine　ROI.　The　results　show　that　the　method　in　this　paper　can　solve　certain　practical　problems　related　to　heart　rate　detection,　with　a　root　mean　square　error　(RMSE)　under　7.64　bpm.