Neighborhood Preserving Embedding (NPE) is an unsupervised linear dimensionality reduction technique which attempts to solve the "out of sample" problem in Locally Linear Embedding (LLE). This is done by introducing a linear transform matrix into LLE, and hence NPE can be perceived as a linear approximation to LLE. In this paper, we modify the original NPE for face recognition by embedding prior class information in the process of neighborhood selection. Intuitively, neighboring points are kept intact if they have the same class label, while avoid points of other classes from entering the neighborhood. We proved experimentally in three face databases, ie. ORL, PIE and FRGC, and with comparisons with other linear and non-linear feature extractors, the intuition underlying the inclusion of class information in NPE works out very advantageously for achieving high recognition performance.