Facial expression recognition (FER) is an active research area that has attracted much attention from both academics and practitioners of different fields. In this paper, we investigate an interesting and challenging issue in FER, where the training and testing samples are from a cross-domain dictionary. In this context, the data and feature distribution are inconsistent, and thus most of the existing recognition methods may not perform well. Given this, we propose an effective dynamic constraint representation approach based on cross-domain dictionary learning for expression recognition. The proposed approach aims to dynamically represent testing samples from source and target domains, thereby fully considering the feature elasticity in a cross-domain dictionary. We are therefore able to use the proposed approach to predict class information of unlabeled testing samples. Comprehensive experiments carried out using several public datasets confirm that the proposed approach is superior compared to some state-of-the-art methods.