  The main challenges of age estimation from facial expression videos lie not
only in the modeling of the static facial appearance, but also in the capturing
of the temporal facial dynamics. Traditional techniques to this problem focus
on constructing handcrafted features to explore the discriminative information
contained in facial appearance and dynamics separately. This relies on
sophisticated feature-refinement and framework-design. In this paper, we
present an end-to-end architecture for age estimation, called Spatially-Indexed
Attention Model (SIAM), which is able to simultaneously learn both the
appearance and dynamics of age from raw videos of facial expressions.
Specifically, we employ convolutional neural networks to extract effective
latent appearance representations and feed them into recurrent networks to
model the temporal dynamics. More importantly, we propose to leverage attention
models for salience detection in both the spatial domain for each single image
and the temporal domain for the whole video as well. We design a specific
spatially-indexed attention mechanism among the convolutional layers to extract
the salient facial regions in each individual image, and a temporal attention
layer to assign attention weights to each frame. This two-pronged approach not
only improves the performance by allowing the model to focus on informative
frames and facial areas, but it also offers an interpretable correspondence
between the spatial facial regions as well as temporal frames, and the task of
age estimation. We demonstrate the strong performance of our model in
experiments on a large, gender-balanced database with 400 subjects with ages
spanning from 8 to 76 years. Experiments reveal that our model exhibits
significant superiority over the state-of-the-art methods given sufficient
training data.
