  With the increasing popularity of convolutional neural networks (CNNs),
recent works on face-based age estimation employ these networks as the
backbone. However, state-of-the-art CNN-based methods treat each facial region
equally, thus entirely ignoring the importance of some facial patches that may
contain rich age-specific information. In this paper, we propose a face-based
age estimation framework, called Attention-based Dynamic Patch Fusion (ADPF).
In ADPF, two separate CNNs are implemented, namely the AttentionNet and the
FusionNet. The AttentionNet dynamically locates and ranks age-specific patches
by employing a novel Ranking-guided Multi-Head Hybrid Attention (RMHHA)
mechanism. The FusionNet uses the discovered patches along with the facial
image to predict the age of the subject. Since the proposed RMHHA mechanism
ranks the discovered patches based on their importance, the length of the
learning path of each patch in the FusionNet is proportional to the amount of
information it carries (the longer, the more important). ADPF also introduces a
novel diversity loss to guide the training of the AttentionNet and reduce the
overlap among patches so that the diverse and important patches are discovered.
Through extensive experiments, we show that our proposed framework outperforms
state-of-the-art methods on several age estimation benchmark datasets.
