Apparent Age Estimation From Face Images Combining General and Children-Specialized Deep Learning Models

Grigory Antipov, Moez Baccouche, Sid-Ahmed Berrani, Jean-Luc Dugelay; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 96-104

Abstract


This work describes our solution in the second edition of the ChaLearn LAP competition on Apparent Age Estimation. We train VGG-16 convolutional neural network on the huge IMDB-Wiki dataset for biological age estimation and then fine-tune it for apparent age estimation using the relatively small competition dataset. We show that the precise age estimation of children is the cornerstone of the competition. Therefore, we integrate a separate "children" VGG-16 network for apparent age estimation of children between 0 and 12 years old in our final solution. The "children" network is fine-tuned from the "general" one. We employ different age encoding strategies for training "general" and "children" networks: the soft one (label distribution encoding) for the "general" network and the strict one (0/1 classification encoding) for the "children" network. Our resulting solution wins the 1st place in the competition significantly outperforming the runner-up.

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[bibtex]
@InProceedings{Antipov_2016_CVPR_Workshops,
author = {Antipov, Grigory and Baccouche, Moez and Berrani, Sid-Ahmed and Dugelay, Jean-Luc},
title = {Apparent Age Estimation From Face Images Combining General and Children-Specialized Deep Learning Models},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2016}
}