Aiming　at　the　spoofing　attacks　for　the　current　face　authentication　systems,　the　traditional　spoofing　attacks　include　displaying　printed　photos　and　replaying　recorded　videos.　With　the　rapid　development　of　three-　dimensional　(3D)　printing　technology,　the　3D　mask　spoofing　attack　is　becoming　a　new　threat.　On　the　basis　of　the　shearlet　transform　and　combining　with　the　3D　geometric　attributes　and　the　local　regional　texture　changes,　a　method　by　utilizing　the　multilayer　autoencoder　network　to　conduct　the　feature　fusion-based　classification　to　identify　the　attack　mask　is　proposed　for　the　3D　mask　spoofing　attack.　The　low-frequency　sub-band　and　several　high-frequency　sub-bands　are　extracted　from　the　3D　image　of　the　target　face　by　the　non　sub-sampled　shearlet　transform　method.　The　scale　space　function　is　used　to　detect,　locate　and　distribute　the　feature　points　and　then　to　generate　feature　operators　in　the　low-frequency　sub-band　.　Then,　the　generated　feature　operators　and　the　texture　features　extracted　from　the　high-frequency　sub-band　are　combined　in　series　and　fed　into　the　stacked　autoencoder　network　and　the　softmax　classifier　to　conduct　the　bottleneck　feature　fusion-based　classification.　The　experimental　results　in　the　BFFD　database　based　on　the　flexible　TPU　material　3D　print　mask　shows　that,　the　multi-feature　fusion　method　added　the　3D　geometric　feature　has　an　obvious　improvement　for　the　accuracy　of　the　anti-spoofing　performance　against　3D　mask　attacks　to　compare　with　the　previous　method　of　using　the　texture　feature　alone.　©　2019　Universitat　zu　Koln.　All　rights　reserved.