Automatic facial age classification and estimation is an interesting and challenging problem, and has many real world applications. The performances of the classification methods may differ depending on the selected training samples. Also using large amount of training samples makes the classification systems more complex and time consuming. In this paper, a novel and a simple age classification method using morph-based age models is presented. The age models representing the common characteristics of age groups are produced using image morphing method. Then age related facial features are extracted with Local Binary Patterns. In the classification phase, ensemble of distance metrics is used to determine the closeness of the test sample to age groups. Then, the results of these metrics are combined with Borda Count voting method to improve the classification performance. Experimental results using the Face and Gesture Recognition Research Network (FGNET) and Park Aging Mind Laboratory (PAL) aging databases show that the proposed method achieves better age classification accuracy when compared to some of the previous methods.