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In this paper we present a dynamic model for simulating face aging process. We adopt a high resolution grammatical face model  and augment it with age and hair features. This model represents all face images by a multi-layer and-or graph and integrates three most prominent aspects related to aging changes: global appearance changes in hair style and shape, deformations and aging effects of facial components, and wrinkles appearance at various facial zones. Then face aging is modeled as a dynamic Markov process on this graph representation which is learned from a large dataset. Given an input image, we firstly compute the graph representation, and then sample the graph structures over various age groups according to the learned dynamic model. Finally we generate new face images with the sampled graphs. Our approach has three novel aspects: (1) the aging model is learned from a dataset of 50,000 adult faces at different ages; (2) we explicitly model the uncertainty in face aging andean sample multiple plausible aged faces for an input image; and (3) we conduct a simple human experiment to validate the simulated aging process.