To　extract　more　information　from　face　images　and　promote　the　recognition　efficiency,　a　novel　2D　face　recognition　approach　is　proposed　for　fusing　multichannel　information　of　face　image.　First,　a　face　image　is　decomposed　into　four　subimages　by　the　Haar　wavelets　transform.　Then,　each　subimage　is　represented　by　a　new　face　representation　approach,　Haar　local　binary　pattern　histogram　(HLBPH).　Finally,　two　novel　match　models　for　face　samples　are　presented　to　balance　the　different　face　regions.　The　linear　weighted　fusion　strategy　to　fuse　the　four　channels＇　facial　information.　Experimental　results　are　tested　on　ORL　and　Yale　face　database,　which　show　that　the　proposed　algorithm　is　effective　and　robust　to　the　facial　pose,　expression,　and　illumination　conditions.