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.
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)