A facial expression recognition method named SS-PLDA is proposed based on Sparse Feature Selection and Probabilistic Linear Discriminant Analysis.The SS-PLDA method contains two steps:1) pick out the most discriminative regions for facial expressions and use these regions to construct a complete facial features set;2) apply Probabilistic Linear Discriminant Analysis Method to separate the useful expression signals from other disturbance information.Therefore

a subspace which only contains expression information is learnt and the expression recognition task is implemented in this subspace.Experimental results on Cohn-Kanade(CK+) database and JAFFE database show that the complete facial features set can improve the performance

and the proposed method outperform the state-of-art ones.