Pose　variation　is　one　of　challenging　problems　in　face　recognition.　Complete　pose　binary　SIFT(CPBS)　was　proposed　to　extract　binary　ASIFT　from　face　images　of　five　poses,　which　is　utilized　for　face　recognition　and　demonstrates　good　performance.　However,　CPBS　has　a　large　data　and　requires　high　computational　cost.　Here,　the　compact　complete　pose　binary　SIFT　(CCPBS)　is　presented　to　address　the　issue.　Five　face　images　with　poses　of　frontal　view,　rotation　left/right　45　and　90　degree　respectively　are　selected　as　gallery　images　of　a　subject.　Firstly,　the　ASIFT　descriptors　of　these　image　are　pooled　together.　Then　the　algorithm　based　on　sparse　representation　are　proposed　to　filter　out　the　ASIFT　descriptors　with　similar　characteristics.　After　that,　the　binary　ASIFT　descriptors　are　extracted　and　the　CCPBS　can　be　obtained.　Face　recognition　is　finished　by　hamming　distance　between　the　probe　face　image　and　the　CCPBS.　Compared　experiments　are　carried　out　on　the　CMU-PIE　face　databases　and　FERET　face　databases.　Experimental　results　show　that　our　approach　can　obtain　higher　recognition　ratio　without　face　alignment　or　landmark　fitting,　which　is　much　better　than　state-of-the-art　algorithms.　Compared　with　CPBS,　the　recognition　ratio　is　reduced　slightly　with　the　reduced　data　of　22.11%　and　17.0%.　©,　2015,　Science　Press.　All　right　reserved.