Some　pose　invariant　face　recognition　approaches　require　preprocessing　such　as　face　alignment　or　landmark　fitting,　which　is　another　unresolved　problem.　SIFT　based　face　recognition　schemes　could　resolve　the　problem　of　constrained　pose　variation　without　such　preprocessing.　we　find　that　the　sift　descriptors　are　robust　to　off-plane　rotation　within　25　degree　and　in-plane　rotation.　Furthermore,　we　propose　complete　pose　binary　SIFT　(CPBS)　to　address　the　issue　of　arbitrary　pose　variation.　First,　five　face　images　with　poses　of　frontal　view,　rotation　left/right　45　and　90　degree　respectively　are　selected　as　gallery　images　of　a　subject.　Then　the　binary　descriptors　of　these　images　are　pooled　together　as　CPBS　of　the　subject.　Face　recognition　is　finished　by　hamming　distance　between　the　probe　face　image　and　the　CPBS.　Experimental　results　on　the　CMU-PIE　and　FERET　face　databases　show　that　our　approach　has　performance　comparable　to　state-of-the-art　approaches,　while　not　requiring　face　alignment　or　landmark　fitting.　©　Springer　International　Publishing　2013.