A Multi-layer Model for Face Aging Simulation

摘要

Face aging simulation is a very complex and challenging task and interests many researchers in the fields of psychology, computer graphics and computer vision due to its widely applications. In this paper, we propose a multi-layer coarse-to-fine face representation and aging simulation and animation algorithm. In the coarse layer, we build a global statistical appearance model for representation and faces are aged based on the learned age trajectory in the appearance space. In the mid layer, we learned a set of age specific coupled dictionaries and the faces are represented and aged via the sparse representation on the learned dictionary. At the fine layer, we sample a lot of patches of facial components and skin zones from images of each age group and use them as the dictionaries to simulate the aging effects of the facial components and wrinkles. We collect a database of 10,050 Chinese passport-type images with different ages for the learning and aging simulation. Experimental results demonstrate the effectiveness of the proposed method.