A Non-generative approach for face recognition across aging

Human faces undergo a lot of change in appearance as they age. Though facial aging has been studied for decades, it is only recently that attempts have been made to address the problem from a computational point of view. Most of these early efforts follow a simulation approach in which matching is performed by synthesizing face images at the target age. Given the innumerable different ways in which a face can potentially age, the synthesized aged image may not be similar to the actual aged image. In this paper, we bypass the synthesis step and directly analyze the drifts of facial features with aging from a purely matching perspective. Our analysis is based on the observation that facial appearance changes in a coherent manner as people age. We provide measures to capture this coherency in feature drifts. Illustrations and experimental results show the efficacy of such an approach for matching faces across age progression.