It　is　well　recognized　that　expressions　can　significantly　change　facial　geometry　that　results　in　a　severe　problem　for　robust　3D　face　recognition.　So　it　is　crucial　for　many　applications　that　how　to　extract　expression-robust　features　to　describe　3D　faces.　In　this　paper,　we　develop　a　novel　3D　face　recognition　algorithm　using　Local　Binary　Pattern　(LBP)　under　expression　varieties,　which　is　an　extension　of　the　LBP　operator　widely　used　in　ordinary　facial　analysis.　First,　to　depict　the　human　face　more　accurately　and　reduce　the　effect　of　facial　local　distortion　for　face　recognition,　a　special　feature-based　3D　face　division　scheme　is　proposed.　Then,　the　LBP　representation　framework　for　3D　faces　is　described,　and　the　facial　depth　and　normal　information　are　extracted　and　encoded　by　LBP,　to　reduce　the　expression　effect.　For　each　face　region,　the　statistical　histogram　is　utilized　to　summarize　the　facial　details,　and　accordingly　three　matching　strategies　are　presented　to　address　the　recognition　task.　Finally,　the　proposed　3D　face　recognition　algorithm　is　tested　on　BJUT-3D　and　FRGC　v2.0　databases,　achieves　promising　results,　and　concludes　that　it　is　feasible　and　valid　to　apply　the　LBP　representation　framework　on　3D　face　recognition.　(C)　2012　Elsevier　B.V.　All　rights　reserved.