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


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.


More detail can easily be written here using Markdown and $\rm \LaTeX$ math code.