According　to　human　vision　theory,　the　image　is　conveyed　from　human　visual　system　to　brain　when　people　have　a　look　at.　Different　from　previous　work,　the　study　reported　in　this　paper　attempts　to　simulate　a　more　real　and　complex　method　for　region　of　interest　(ROI)　detection　and　quantitatively　analyze　the　correlation　between　users＇　visual　perception　and　ROI.　In　this　paper,　a　visual　perception　model-based　ROI　detection　is　proposed,　which　can　be　realized　with　an　ordinary　web　camera.　Visual　perception　model　employs　a　combination　of　visual　attention　model　and　gaze　tracking　data　to　objectively　detect　ROIs.　The　work　includes　pre-ROI　estimation　using　visual　attention　model,　gaze　data　collection　and　ROI　detection.　Pre-ROIs　are　segmented　by　the　visual　attention　model.　Since　eye　feature　extraction　is　critical　to　the　accuracy　and　performance　of　gaze　tracking,　adaptive　eye　template　and　neural　network　are　employed　to　predict　gaze　points.　By　computing　the　density　of　the　gaze　points,　ROIs　are　ranked.　Experimental　results　show　that　the　accuracy　of　our　ROI　detection　method　can　be　raised　as　high　as　97%　and　it　is　also　demonstrated　that　our　model　can　efficiently　adapt　to　users＇　interests　and　match　the　objective　ROI.