  Age estimation from a single face image has been an essential task in the
field of human-computer interaction and computer vision, which has a wide range
of practical application values. Accuracy of age estimation of face images in
the wild is relatively low for existing methods, because they only take into
account the global features, while neglecting the fine-grained features of
age-sensitive areas. We propose a novel method based on our attention long
short-term memory (AL) network for fine-grained age estimation in the wild,
inspired by the fine-grained categories and the visual attention mechanism.
This method combines the residual networks (ResNets) or the residual network of
residual network (RoR) models with LSTM units to construct AL-ResNets or AL-RoR
networks to extract local features of age-sensitive regions, which effectively
improves the age estimation accuracy. First, a ResNets or a RoR model
pretrained on ImageNet dataset is selected as the basic model, which is then
fine-tuned on the IMDB-WIKI-101 dataset for age estimation. Then, we fine-tune
the ResNets or the RoR on the target age datasets to extract the global
features of face images. To extract the local features of age-sensitive
regions, the LSTM unit is then presented to obtain the coordinates of the
agesensitive region automatically. Finally, the age group classification is
conducted directly on the Adience dataset, and age-regression experiments are
performed by the Deep EXpectation algorithm (DEX) on MORPH Album 2, FG-NET and
15/16LAP datasets. By combining the global and the local features, we obtain
our final prediction results. Experimental results illustrate the effectiveness
and robustness of the proposed AL-ResNets or AL-RoR for age estimation in the
wild, where it achieves better state-of-the-art performance than all other
convolutional neural network.
