Apparent age estimation from face images combining general and children-specialized deep learning models

Antipov, Grigory; Baccouche, Moez; Berrani, Sid-Ahmed; Dugelay, Jean-Luc
CVPRW 2016, 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, June 26th-July 1st, 2016, Las Vegas, USA

Best Paper Award and 1st Place Award

This work describes our solution in the second edition of the ChaLearn LAP competition on Apparent Age Estimation. Starting from a pretrained version of the VGG-16 convolutional neural network for face recognition, we train it 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. Finally, we highlight the importance of the state-of-the-art face detection and face alignment for the final apparent age estimation. Our resulting solution wins the 1st place in the competition significantly outperforming the runner-up.

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Digital Security
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