  Age estimation is a difficult task which requires the automatic detection and
interpretation of facial features. Recently, Convolutional Neural Networks
(CNNs) have made remarkable improvement on learning age patterns from benchmark
datasets. However, for a face "in the wild" (from a video frame or Internet),
the existing algorithms are not as accurate as for a frontal and neutral face.
In addition, with the increasing number of in-the-wild aging data, the
computation speed of existing deep learning platforms becomes another crucial
issue. In this paper, we propose a high-efficient age estimation system with
joint optimization of age estimation algorithm and deep learning system.
Cooperated with the city surveillance network, this system can provide age
group analysis for intelligent demographics. First, we build a three-tier fog
computing architecture including an edge, a fog and a cloud layer, which
directly processes age estimation from raw videos. Second, we optimize the age
estimation algorithm based on CNNs with label distribution and K-L divergence
distance embedded in the fog layer and evaluate the model on the latest wild
aging dataset. Experimental results demonstrate that: 1. our system collects
the demographics data dynamically at far-distance without contact, and makes
the city population analysis automatically; and 2. the age model training has
been speed-up without losing training progress or model quality. To our best
knowledge, this is the first intelligent demographics system which has
potential applications in improving the efficiency of smart cities and urban
living.
