In this paper we present a compositional dynamic model for face aging. By augmenting the high resolution hierarchic face model studied in Xu et al [40], [41] with aging and hair features, this compositional model represents all face images by a hierarchical And-Or graph [5]. The And nodes decompose face into parts and primitives at three levels from coarse to fine. The first level describes the low resolution face image including the hair-style and face appearance. The second level refines the first level by modeling shape, intensity and other variability of facial components. The third level accounts for the skin marks and wrinkles in different facial zones. Correspondingly three prominent aspects of aging related changes are integrated in this model: global appearance changes in hair style and face shape, deformations and aging effects of facial components, and wrinkles in various facial zones. Among the nodes at the same level, spatial relationships and constraints are modeled to force the validness of the configurations. At each level, the Or nodes describe the alternative sub-configurations, which model the diversity of human face across all ages.After choosing alternative of each Or nodes, the And-Or graph turns into a parse graph, which represents a specific instance of human face. Then face aging is modeled as a dynamic Markov process on this graph representation. The evolution of a graph includes not only the changes of its topology representing the emergence of features related to a new age but also the changes of its nodes attributes accounting for the extent of the aging status. The parameters of the dynamic model are learned from a large annotated dataset in a supervised way. Based on this model, we present an algorithm of synthesizing aged face images from a young face image: given an input image, we firstly compute its parse graph representation, and then sample the graph parameters over various age groups according to the learned dynamic model, including abrupt changes and continuous changes. Our sampling process accounts for the uncertainty in face aging by generating multiple plausible aged faces according to the stochastic model. Finally we generate new face images with the sampled parse graphs. We study two criteria to evaluate the synthesized aging results and conduct two human experiments on the aged image sequences synthesized by our algorithm. The results validate the performance of our model and aging algorithm.