In　this　paper,　a　novel　face　representation　approach,　Haar　Local　Binary　Pattern　histogram　(HLBPH),　is　proposed　to　represent　the　face　images.　First,　the　face　image　is　decomposed　into　four-channel　subimages　in　frequency　domain　by　Haar　wavelet　transform,　and　then　the　LBP　operator　is　applied　on　each　subimage　to　extract　the　face　features.　After　that,　a　novel　Haar　LBP　representation　method　(HLBPH)　based　on　two-layer　weighted　fusion　scheme　is　presented　to　balance　the　face　block　regions　for　LBP,　to　fuse　the　multi-channel　face　features.　In　recognition　stage,　the　Chi　square　statistic(χ2)　is　employed　as　the　dissimilarity　measure　for　face　histograms　represented　by　HLBPH.　Finally,　the　proposed　algorithm　is　tested　on　ORL　and　Yale　face　database,　and　achieve　satistying　results　to　pose,　expression　and　illumination　variations.　©　2010　IEEE.