In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for face feature extraction and face recognition, which is based on graph embedded learning and under the Fisher discriminant analysis framework. In MMDA, the within-class graph and between-class graph are designed to characterize the within-class compactness and the between-class separability, respectively, seeking for the discriminant matrix that simultaneously maximizing the between-class scatter and minimizing the within-class scatter. In addition, the within-class graph can also represent the sub-manifold information and the between-class graph can also represent the multi-manifold information. The proposed MMDA is examined by using the FERET face database, and the experimental results demonstrate that MMDA works well in feature extraction and lead to good recognition performance.