Age Estimation Using Aging/Rejuvenation Features With Device-Edge Synergy

Estimating human age is a challenging task in computer vision and most researchers are trying to make age estimation via a static facial image. However, it ignores the fact that the age of a person is the specific representation of aging. In this paper, we attempt to explore the aging/rejuvenation (AR) characteristics of faces for age estimation and we called the whole network as AR-Net. Firstly, we use GAN model for learning a manifold of the aging/rejuvenation process to a face dataset with preserving personalized face features (
e.g.,
gender, race). Secondly, we seek the correlated aging/rejuvenation characteristics from a narrow age interval, (
e.g.,
((0-100)
$\rightarrow $
(0-5), (5-10),…, (90-100)). Thirdly, the fine-tuned GAN is used to generate aging/rejuvenation features of all age groups and these features are applied to train corresponding ELM regressors. AR-Net is deployed on every edge server, and all AR-Nets are trained offline. Afterwards, our AR-Net is constantly updated based on the face dataset collected by the edge sensors. Finally, enormous experiments on Morph-II, CACD, and captured facial dataset have been conducted to verify the performance of our fine-tuned AR-Net and the experimental results show that the approach enhanced than the current state of the art methods.