A　novel　glasses-faces　recognition　method　based　on　the　2D　unsupervised　geodesic　discriminant　projection　(2DUGDP)　technique　is　presented　in　this　paper.　Based　on　the　virtual　samples,　discriminable　features　will　be　obtained　by　analyzing　the　difference　of　faces　with　variety　eyeglasses.　This　feature　characterizes　the　local　scatter　as　well　as　the　nonlocal　scatter,　seeking　to　find　a　projection　that　simultaneously　maximizes　the　nonlocal　scatter　and　minimizes　the　local　scatter.　The　projection　ensures　the　distance　of　samples　remain　close　for　near　samples,　and　separate　for　far　samples.　Face　space　is　regarded　as　a　nonlinear　instructure　embedded　in　the　high　dimensional　space.　Geodesic　distance　is　employed　to　model　the　intrinsic　structure　of　the　manifold.　The　method　is　applied　to　glasses-faces　recognition　and　examination　using　the　CAS-PEAL,　FERET　face　databases.　Results　show　that　2DUDP　outperforms　other　methods.