  Facial age estimation is an important and challenging problem in computer
vision. Existing approaches usually employ deep neural networks (DNNs) to fit
the mapping from facial features to age, even though there exist some noisy and
confusing samples. We argue that it is more desirable to distinguish noisy and
confusing facial images from regular ones, and alleviate the interference
arising from them. To this end, we propose self-paced deep regression forests
(SP-DRFs) -- a gradual learning DNNs framework for age estimation. As the model
is learned gradually, from simplicity to complexity, it tends to emphasize more
on reliable samples and avoid bad local minima. Moreover, the proposed
capped-likelihood function helps to exclude noisy samples in training,
rendering our SP-DRFs significantly more robust. We demonstrate the efficacy of
SP-DRFs on Morph II and FG-NET datasets, where our model achieves
state-of-the-art performance.
