Sparse　representation　for　face　recognition　has　been　exploited　in　past　years.　Several　sparse　representation　algorithms　have　been　developed.　In　this　paper,　a　novel　eyeglasses-face　recognition　approach,　SEMD　Based　Sparse　Gabor　Representation,　is　proposed.　Firstly,　for　a　robust　representation　to　misalignment,　a　sparse　Gabor　representation　is　proposed.　Secondly,　spatially　constrained　earth　mover＇s　distance　is　employed　instead　of　Euclidean　distance　to　measure　the　similarity　between　original　data　and　reconstructed　data.　The　proposed　algorithm　for　eyeglasses-face　recognition　has　been　evaluated　under　different　eyeglasses-face　databases.　The　experimental　results　reveal　that　the　proposed　approach　is　validity　and　has　better　recognition　performance　than　that　obtained　using　other　traditional　methods.　©　2011　Springer-Verlag.