In practical facial expression recognition systems， recognition rates will drop significantly when the data are collected from different scenarios. To tackle this problem， in this paper， we propose a sparse subspace transfer learning for cross-domain facial expression recognition. Firstly， inspired by the idea of sparse reconstruction， we aim to learn a common projection matrix， and impose an L2,1-norm constraint on the reconstruction coefficient matrix. Secondly， we introduce the Laplacian regularization to preserve the local discriminative structure. Lastly， by utilizing the rich label information of source domain， we tend to project the source samples into a subspace guided by the label information. We conduct extensive experiments on three popular facial expression datasets. The results show that our proposed method can outperform several state-of-the-art subspace transfer learning methods in facial expression recognition.