A　novel　nonlinear　face　recognition　method　named　GPPface　is　proposed　in　this　paper.　GPPface　is　based　on　nonlinear　dimensionality　reduction　algorithm,　Geodesic　Preserving　Projection　(GPP).　As　face　images　are　regarded　to　be　embedded　in　a　nonlinear　space,　GPP　is　presented　to　nonlinearly　map　high-dimensional　face　images　to　low-dimensional　feature　space.　GPP　overcomes　the　weaknesses　of　traditional　linear　and　nonlinear　dimensionality　reduction　algorithms,　well　preserves　the　intrinsic　structure　of　the　manifold　and　can　fast　and　efficiently　map　new　sample　point　to　feature　space.　To　recover　space　structure　of　face　images　and　tackle　small　sample　size　problem,　3D　morphable　model　is　developed　to　derive　multiple　images　of　a　person　from　a　single　image.　Experimental　results　on　ORL　and　PIE　face　databases　show　that　our　method　makes　impressive　performance　improvement　compared　with　conventional　face　recognition　methods.　©　2006　IEEE.