With　their　growing　popularity　and　widespread　applications,　face　recognition　systems　are　attracting　more　attention　from　attackers.　Thus,　face　presentation　attack　detection　has　emerged　as　an　important　research　topic　in　recent　years.　Existing　methods　for　face　presentation　attack　detection　are　affected　by　different　cameras　and　display　devices,　and　their　performance　is　degraded　in　cross-database　testing.　In　this　paper,　we　propose　a　face　presentation　attack　detection　scheme　that　fuses　multi-perspective　dynamic　features.　One　feature　is　the　globally　extracted　temporal　motion　pattern　of　a　face　in　a　video.　This　involves　mapping　the　local　and　global　motion　information　of　the　face　in　the　video　into　a　single　image.　The　motion　patterns　of　genuine　and　fake　faces　are　different,　and　these　patterns　are　independent　of　cameras　and　display　devices.　Another　feature　is　the　visual　rhythm　of　noise　patterns,　which　differs　significantly　between　single　and　secondary　imaging.　The　proposed　scheme　fuses　these　two　features　at　the　decision　level.　Cross-database　tests　were　conducted　among　the　CASIA-FASD,　MSU-MFSD　and　Replay-Attack　databases.　The　experimental　results　show　that　the　proposed　scheme　outperforms　state-of-the-art　algorithms.