  Convolutional Neural Networks (CNN) have been applied to age-related research
as the core framework. Although faces are composed of numerous facial
attributes, most works with CNNs still consider a face as a typical object and
do not pay enough attention to facial regions that carry age-specific feature
for this particular task. In this paper, we propose a novel CNN architecture
called Fusion Network (FusionNet) to tackle the age estimation problem. Apart
from the whole face image, the FusionNet successively takes several
age-specific facial patches as part of the input to emphasize the age-specific
features. Through experiments, we show that the FusionNet significantly
outperforms other state-of-the-art models on the MORPH II benchmark.
