Estimating aging pattern by aging increment distribution for re-rendering of facial age effects

Abstract

Simulating facial aging effects is a challenge task because of the difficulties in understanding and modeling the aging pattern. In this paper, a novel aging model called Aging Increment Distribution Function was proposed to model the age progression in the statistical appearance model space. The trajectory of face samples is learned to build the distribution function with free shape. So it has finer resolution to reveal the underlying aging pattern. Based on modeling the increment of appearance parameter, an analytical framework was formulated to re-render the given face image onto any other age within the maximum age span of training samples. In experiment, the MORPH face database was used to train the aging model, which has been further applied to re-rendering of age effects. Both aging and rejuvenating simulation results presented similar effects comparing to the real images, which verified the effectiveness of proposed method. © Springer-Verlag Berlin Heidelberg 2007.