In　face　recognition,　Low　Resolution　(LR)　images　will　lead　to　the　decline　of　the　recognition　rate.　In　this　paper,　we　propose　a　novel　recognition　oriented　feature　hallucination　method　to　map　the　features　of　a　LR　facial　image　to　its　High　Resolution　(HR)　version.　We　extract　the　principal　component　analysis　(PCA)　features　of　LR　and　HR　face　images.　Then,　canonical　correlation　analysis　is　applied　to　establish　the　coherent　subspaces　between　the　PCA　features　of　the　LR　and　HR　face　images.　Furthermore,　a　recognition　rate　guided　prediction　model　is　proposed　to　map　the　LR　features　to　the　HR　version,　which　is　employed　an　adaptive　Piecewise　Kernel　Partial　Least　Squares　(P-KPLS)　predictor.　Finally,　a　weighted　combination　of　the　hallucinated　PCA　features　and　the　Local　Binary　Pattern　Histogram　(LBPH)　features　are　adopted　for　face　recognition.　Experimental　results　show　that　the　proposed　method　has　a　superior　recognition　rate.　©　Springer　International　Publishing　AG　2016.