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Age estimation is a difficult task which requires the automatic detection andinterpretation of facial features. Recently, Convolutional Neural Networks(CNNs) have made remarkable improvement on learning age patterns from benchmarkdatasets. 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, thecomputation speed of existing deep learning platforms becomes another crucialissue. In this paper, we propose a high-efficient age estimation system withjoint optimization of age estimation algorithm and deep learning system.Cooperated with the city surveillance network, this system can provide agegroup analysis for intelligent demographics. First, we build a three-tier fogcomputing architecture including an edge, a fog and a cloud layer, whichdirectly processes age estimation from raw videos. Second, we optimize the ageestimation algorithm based on CNNs with label distribution and K-L divergencedistance embedded in the fog layer and evaluate the model on the latest wildaging dataset. Experimental results demonstrate that: 1. our system collectsthe demographics data dynamically at far-distance without contact, and makesthe city population analysis automatically; and 2. the age model training hasbeen speed-up without losing training progress or model quality. To our bestknowledge, this is the first intelligent demographics system which haspotential applications in improving the efficiency of smart cities and urbanliving.

Speeding-up Age Estimation in Intelligent Demographics System via Network Optimization