Remote　photoplethysmography　(rPPG),　which　aims　at　measuring　heart　activities　without　any　contact,　has　great　potential　in　many　applications.　The　emergence　of　novel　coronavirus　pneumonia　COVID-19　has　attracted　worldwide　attentions.　Contact　photoplethysmography　(cPPG)　methods　need　to　contact　the　detection　equipment　with　the　patient,　which　may　accelerate　the　spread　of　the　epidemic.　In　the　future,　the　non-contact　heart　rate　detection　will　be　an　urgent　need.　However,　existing　heart　rate　measuring　methods　from　facial　videos　are　vulnerable　to　the　less-constrained　scenarios　(e.g.,　with　head　movement　and　wearing　a　mask).　In　this　paper,　we　proposed　a　method　of　heart　rate　detection　based　on　eye　location　of　region　of　interest　(ROI)　to　solve　the　problem　of　missing　information　when　wearing　masks.　Besides,　a　model　to　filter　outliers　based　on　residual　network　was　conceived　first　by　us　and　the　better　heart　rate　measurement　accuracy　was　generated.　To　validate　our　method,　we　also　created　a　mask　dataset.　The　results　demonstrated　that　after　using　our　method　for　correcting　the　heart　rate　(HR)　value　measured　with　the　traditional　method,　the　accuracy　reaches　4.65　bpm,　which　is　0.42　bpm　higher　than　that　without　correction.