Facial Aging Simulation via Tensor Completion

In aging simulation, the most essential requirements are (1) human identity should remain stable in texture synthesis; and (2) the texture synthesized is expected to accord with human cognitive perception in aging. In this paper, we address the problem of face aging simulation by using a tensor completion based method. The proposed method is composed of two steps. In the first stage, Active Appearance Models (AAM) is applied to facial images to normalize pose variations. In the second stage, the tensor completion based aging simulation method is adopted to synthesize aging effects on facial images. By introducing age and identity prior information in the tensor space, human identity is mostly protected during the aging procedure and proper textures are generated to simulate the aged appearance. Experimental results achieved on the FG-NET database are not only in the age as subjective expectation, but also reserve the person specific cues, which demonstrates the effectiveness of the proposed method.