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The main challenges of age estimation from facial expression videos lie notonly in the modeling of the static facial appearance, but also in the capturingof the temporal facial dynamics. Traditional techniques to this problem focuson constructing handcrafted features to explore the discriminative informationcontained in facial appearance and dynamics separately. This relies onsophisticated feature-refinement and framework-design. In this paper, wepresent an end-to-end architecture for age estimation, called Spatially-IndexedAttention Model (SIAM), which is able to simultaneously learn both theappearance and dynamics of age from raw videos of facial expressions.Specifically, we employ convolutional neural networks to extract effectivelatent appearance representations and feed them into recurrent networks tomodel the temporal dynamics. More importantly, we propose to leverage attentionmodels for salience detection in both the spatial domain for each single imageand the temporal domain for the whole video as well. We design a specificspatially-indexed attention mechanism among the convolutional layers to extractthe salient facial regions in each individual image, and a temporal attentionlayer to assign attention weights to each frame. This two-pronged approach notonly improves the performance by allowing the model to focus on informativeframes and facial areas, but it also offers an interpretable correspondencebetween the spatial facial regions as well as temporal frames, and the task ofage estimation. We demonstrate the strong performance of our model inexperiments on a large, gender-balanced database with 400 subjects with agesspanning from 8 to 76 years. Experiments reveal that our model exhibitssignificant superiority over the state-of-the-art methods given sufficienttraining data.

Attended End-to-end Architecture for Age Estimation from Facial Expression Videos