The　region　of　interest　will　change　according　to　the　task,　even　in　the　same　situation.　In　the　study,　a　method　for　region　of　interest　detection　within　a　computer　screen　using　a　monocular　camera　is　proposed.　In　contrast　to　gaze　tracking　techniques　that　require　particular　devices　(e.g.,　an　eye　tracker　and　RGB-D　device)　meanwhile　complex　calibration,　a　cheap　and　more　convenient　monocular　camera　is　used　in　this　study　to　solves　the　eye　gaze　tracking　problem.　Firstly,　Human　face　is　detected　in　a　real-time　video　sequence　using　HoG　features.　Then,　the　landmarks　around　the　eyes,　which　reflect　the　gaze　position,　are　extracted.　Next,　the　iris　centers　are　detected　in　the　eye　region.　In　order　to　reduce　the　gaze　error　caused　by　head　movement,　a　three-dimensional　head　model　is　proposed　to　estimate　head　pose.　Finally,　the　eye　region　is　tracked　by　calculating　the　eye　vectors　and　head　movement.　Experiments　were　performed　to　evaluate　the　face　detection,　landmarks,　iris　detection,　eye　movement　estimation,　and　head　pose　estimation　on　databases　such　as　the　Hong　Kong,　BioID,　and　Boston　University　head　pose　databases.　Besides,　experiments　for　gaze　tracking　were　performed　for　a　real-time　video　sequence.　Deviation　is　calculated　using　Euclidean　distance　between　the　real　and　estimated　points.　The　results　show　that　the　method　achieves　an　average　error　of　1.85　degrees　with　head　fixed　and　3.58　degrees　with　head　movement　in　the　range　of　-45　degrees　and　45　degrees.　The　requirement　is　detecting　the　user＇s　attention　in　the　screen　area.　Our　method　can　reach　the　same　level　to　the　other　methods,　even　though　the　accuracy　is　not　state-of-the-art.　Meanwhile,　as　we　all　know　not　only　a　specific　point　is　concerned　but　also　a　region　area　according　to　the　characteristics　of　human　eye　imaging,　thus　the　proposed　method　can　meet　the　requirements　of　demand.