Face　presentation　attack　detection　(PAD)　has　become　a　key　component　in　face-based　application　systems.　Typical　face　de-spoofing　algorithms　estimate　the　noise　pattern　of　a　spoof　image　to　detect　presentation　attacks.　These　algorithms　are　device-independent　and　have　good　generalization　ability.　However,　the　noise　modeling　is　not　very　effective　because　there　is　no　ground　truth　(GT)　with　identity　information　for　training　the　noise　modeling　network.　To　address　this　issue,　we　propose　using　the　bona　fide　image　of　the　corresponding　subject　in　the　training　set　as　a　type　of　GT　called　appr-GT　with　the　identity　information　of　the　spoof　image.　A　metric　learning　module　is　proposed　to　constrain　the　generated　bona　fide　images　from　the　spoof　images　so　that　they　are　near　the　appr-GT　and　far　from　the　input　images.　This　can　reduce　the　influence　of　imaging　environment　differences　between　the　appr-GT　and　GT　of　a　spoof　image.　Extensive　experimental　results　demonstrate　that　the　reconstructed　bona　fide　image　and　noise　with　high　discriminative　quality　can　be　clearly　separated　from　a　spoof　image.　The　proposed　algorithm　achieves　competitive　performance　.　(c)　2020　Published　by　Elsevier　B.V.