Apparent Age Estimation with CNN

DOI

10.2991/icmmita-16.2016.30How to use a DOI?

Keywords

age estimation; convolutional neural networks ;IMDB-WIKI dataset .

Abstract

Apparent age estimation from face image has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. In this paper ,we tackle the estimation of apparent age in still face images with deep convolutional neural networks (CNN).Our convolutional neural network use the GoogLeNet architecture, add batch normalization layer after each ReLU operation and remove all the dropout operations to accelerate the convergence of this very large-scale deep network. In addition, due to the limited number of apparent age annotated images, we train the deep models with several datasets in a cascaded way. Firstly, We pre-train a real age estimation model using IMDB-WIKI dataset, and then ne-tune the deep model with combined dataset with multiple real-age labeled databases .Finally, the apparent age data from the challenge are used to ne-tune the deep model parameters for apparent age estimation.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Zhiqin Zhang
PY  - 2017/01
DA  - 2017/01
TI  - Apparent Age Estimation with CNN
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 152
EP  - 157
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.30
DO  - 10.2991/icmmita-16.2016.30
ID  - Zhang2017/01
ER  -