Dictionary Learning and sparse coding methods have been widely used in computer vision with applications to face and object recognition. A common challenge when performing expression recognition is that face similarities may confound the expression recognition process. An approach to deal with this problem is to learn expression specific dictionaries, so that each atom corresponds to one expression class. However, even when employing expression specific dictionaries, it is likely that two atoms from two sub-dictionaries share common characteristics due to facial similarities. In this paper, we consider a joint dictionary that captures common facial attributes, and class-specific dictionaries that are used to classify different expressions. We investigate three dictionary learning methods for sparse representation classification: one that learns a global dictionary based on K-SVD, one that learns expression specific dictionaries based on Fisher Discrimination Dictionary Learning (FDDL), and one that learns a shared as well as expression specific dictionaries based on Dictionary Learning Separating Commonality and Particularity (DL-COPAR). We demonstrate the effectiveness of the shared dictionary learning approach on the extended Cohn-Kanade database where DL-COPAR outperforms FDDL and KSVD by a significant margin.