Locally linear embedding (LLE) algorithm has no direct relationship with the classification. Meanwhile, the recognition effect is decreased when the LLE algorithm is affected by different facial expressions, illumination and pose, etc.,and the distribution of the original sample is usually nonlinear and complex. Therefore, an efficient dimensional reduction and classification algorithm is presented, that is fuzzy difference embedding projection (FDEP) algorithm. The FDEP algorithm constructs different radiograms to characterize the local and the global structure information using fuzzy membership degree (fuzzy sets) under fuzzy thinking, and then uses the maximum margin criterion (MMC) to construct the objective function for avoiding the ″small-size sample″problem. Finally, the algorithm solves the constrained optimization by Lagrange operators. The FDEP algorithm maintains the original neighbor relations for neighboring data points of the same class and is also crucial to keep away neighboring data points of different classes. The results of face recognition experiments on ORL, Yale and AR face databases demonstrate the effectiveness of the FDEP algorithm.