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.

翻译：随着革命神经网络(CNNs)越来越受欢迎,最近关于基于面部年龄估计的工作利用这些网络作为主干线。然而,以CNN为基础的最先进的方法对每个面部区域一视同仁,从而完全忽视某些面部补丁的重要性,这些面部补丁可能包含丰富的特定年龄信息。在本文件中,我们提议了一个基于面部的年龄估计框架,称为“基于关注的动态补丁聚合(ADPF) ” 。在ADPF中,实施了两个单独的CNN,即“关注网”和“FusionNet ” 。“关注网”通过使用新颖的、引导式的多层混合关注(RMHHA)机制,动态定位和排列了特定年龄的补丁。“FusionNet”利用所发现的补丁和面部面图与面部图像一起用来预测该主题的年限。由于拟议的RMHHA机制按其重要性排列了所发现的补丁的长度与它所含信息数量成比例成正成比例(时间越长,更重要的是)。ADPFPFPFPER还引入了一个新的多样性损失,用以指导对关注网络的培训,我们所发现的重要年龄框架的模型。