  Age estimation is an essential challenge in computer vision. With the
advances of convolutional neural networks, the performance of age estimation
has been dramatically improved. Existing approaches usually treat age
estimation as a classification problem. However, the age labels are ambiguous,
thus make the classification task difficult. In this paper, we propose a simple
yet effective approach for age estimation, which improves the performance
compared to classification-based methods. The method combines four
classification losses and one regression loss representing different class
granularities together, and we name it as Age-Granularity-Net. We validate the
Age-Granularity-Net framework on the CVPR Chalearn 2016 dataset, and extensive
experiments show that the proposed approach can reduce the prediction error
compared to any individual loss. The source code link is
https://github.com/yipersevere/age-estimation.
