We have developed new dimensionality reduction methods, extended from locality preserving projection (LPP), to estimate age using facial images. LPP seeks a linear transformation matrix such that optimally preserves the neighborhood structure of the data. Our focus has been on expanding LPP by making use of class label information. Specifically, one of our ideas is to assign weights only to the data with close class labels. A local scaling method is used for each class to compute the LPP affinity matrix. Another idea is to assign large weights to two samples with close class labels, i.e., close ages. By doing this, class label information for original data (i.e., age information) can be preserved. We thus call this "class distance weighted linear preserving projection" (CDLPP). Experimental results on a large database showed that CDLPP has more discriminative power than conventional methods such as PCA and LPP.