With　the　development　of　E-Commerce,　biometric　based　on-line　authentication　is　more　competitive　and　is　paid　more　attentions.　It　brings　about　one　of　hot　issues　of　liveness　detection　recently.　In　this　paper,　we　propose　a　liveness　detection　scheme　to　combine　Fourier　statistics　and　local　binary　pattern　(LBP).　First,　The　Gamma　correction　and　DoG　filtering　are　utilized　to　reduce　the　illumination　variation　and　to　preserve　the　key　information　of　the　image.　Then　the　Fourier　statistics　and　LBP　are　combined　together　to　form　a　new　feature　vector.　Finally,　a　SVM　classifier　is　trained　to　discriminate　the　live　and　forge　face　image.　The　experimental　results　on　the　NUAA　demonstrate　that　the　proposed　scheme　is　efficient　and　robust.　©　Springer　International　Publishing　Switzerland　2014.