With　the　rapid　development　of　face　recognition　systems　in　various　practical　applications,　numerous　face　spoofing　attacks　under　different　environment　and　devices　have　emerged.　The　countermeasure　of　face　spoofing　attacks　in　cross-database　have　caused　increasing　attention.　This　paper　proposes　a　face　spoofing　detection　method　with　motion　analysis　based　cross-database　voting.　We　employ　the　consistency　motion　information　of　different　databases　like　eye-blink,　mouth　movements　and　facial　expression　etc.　Then　the　motion　information　maps　of　a　video　is　classified　to　real　or　fake　by　CNN　model.　Furthermore,　cross-database　voting　strategy　is　constructed　to　transfer　motion　characteristics　from　a　database　to　another　for　face　spoofing　inference.　Experimental　results　demonstrate　that　the　proposed　method　outperforms　its　comparisons　taking　benefits　of　motion　analysis　based　CNN　classification　and　cross-database　voting.　©　2017,　Springer　International　Publishing　AG.