In　this　paper,　an　orientation　and　scale　invariant　binary　descriptor　is　proposed,　which　can　be　used　in　key-points　matching　systems.　Conventionally,　a　binary　descriptor　is　generated　by　comparing　the　intensities　of　pixels　directly,　such　as　those　in　Binary　Robust　Independent　Elementary　Features　(BRIEF)　and　Oriented　FAST　and　Rotated　BRIEF　(ORB).　However,　comparing　intensities　of　pixels　may　lose　the　texture　information　in　the　region　of　interest,　and　lead　to　a　high　false　match　rate　in　a　practical　application.　In　our　proposed　method,　the　region　of　interest　is　segmented　into　grid　cells　and　then　the　binary　Haar　wavelet　responses　are　computed　to　store　the　texture　information　of　the　patch.　Concretely,　the　texture　information　in　each　cell　is　expressed　by　the　horizontal　and　vertical　gradients　and　the　polarity　of　intensity　changes　which　are　indicated　by　four　components　of　Haar　wavelet　response.　The　binary　descriptor　is　generated　by　comparing　the　Haar　wavelet　response　in　each　pair　of　grid　cells.　Furthermore,　to　be　scale　and　orientation　invariant,　the　patch　of　key-points　is　rotated　to　the　primary　direction　of　the　centroid　vector　in　the　image　pyramid.　Extensive　experimental　results　show　that　our　descriptor　significantly　outperforms　other　five　state-of-the-art　binary　descriptors　in　key-point　matching　systems.　The　average　percentage　of　correct　matches　of　our　method　is　32.79%　higher　than　that　for　FREAK　and　5.31%　higher　than　that　for　LDB.　©　2015　Asia-Pacific　Signal　and　Information　Processing　Association.