Apparent Age Estimation from Face Images Combining General and Children-Specialized Deep Learning Models - Archive ouverte HAL Access content directly
Conference Papers Year : 2016

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

Grigory Antipov
Sid-Ahmed Berrani
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Jean Luc Dugelay
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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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Dates and versions

hal-01380587 , version 1 (13-10-2016)

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  • HAL Id : hal-01380587 , version 1

Cite

Grigory Antipov, Moez Baccouche, Sid-Ahmed Berrani, Jean Luc Dugelay. Apparent Age Estimation from Face Images Combining General and Children-Specialized Deep Learning Models. Atelier international, Jun 2016, Las Vegas, United States. ⟨hal-01380587⟩

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