Face　presentation　attack　detection　methods　based　on　deep　learning　have　achieved　noticeable　results.　However,　such　methods　tend　to　over-emphasize　a　certain　local　area,　which　limits　their　performance　against　traditional　attacks,　and　makes　the　system　vulnerable　to　adversarial　example　attacks.　To　utilize　more　information　of　the　input　and　enhance　the　robustness　of　face　presentation　attack　detection　methods　against　adversarial　examples,　this　paper　proposes　multi-regional　convolutional　neural　networks,　and　introduces　the　concept　of　local　classification　loss　to　local　patches,　so　as　to　utilize　the　input　information　in　the　entire　face　region　and　to　avoid　over-emphasizing　certain　local　areas.　Experimental　results　demonstrate　that　the　proposed　method　is　more　robust　against　adversarial　example　attacks,　and　its　performance　against　traditional　attacks　is　also　improved　compared　to　existing　methods.　(c)　2020　Published　by　Elsevier　B.V.