In　this　paper,　we　present　a　novel　eigentransformation　based　algorithm　for　face　hallucination.　The　traditional　eigentransformation　method　is　a　linear　subspace　approach,　which　represents　an　image　as　a　linear　combination　of　training　samples.　Consequently,　it　cannot　effectively　represent　the　relationship　between　the　low　resolution　facial　images　and　the　corresponding　high-resolution　version.　In　our　algorithm,　a　Kernel　Partial　Least　Squares　(KPLS)　predictor　is　introduced　into　the　eigentransformation　model　for　solving　the　High　Resolution　(HR)　image　form　a　Low　Resolution　(LR)　facial　image.　We　have　compared　our　proposed　method　with　some　current　Super　Resolution　(SR)　algorithms　using　different　zooming　factors.　Experimental　results　show　that　our　algorithm　provides　improved　performances　over　the　compared　methods　in　terms　of　both　visual　quality　and　numerical　errors.　©　2012　IEEE.