TAA-GCN: A Temporally Aware Adaptive Graph Convolutional Network for Age
  Estimation
Matthew Korban Peter Young Scott T. Acton
This paper proposes a novel age estimation algorithm, the Temporally-Aware
Adaptive Graph Convolutional Network (TAA-GCN). Using a new representation
based on graphs, the TAA-GCN utilizes skeletal, posture, clothing, and facial
information to enrich the feature set associated with various ages. Such a
novel graph representation has several advantages: First, reduced sensitivity
to facial expression and other appearance variances; Second, robustness to
partial occlusion and non-frontal-planar viewpoint, which is commonplace in
real-world applications such as video surveillance. The TAA-GCN employs two
novel components, (1) the Temporal Memory Module (TMM) to compute temporal
dependencies in age; (2) Adaptive Graph Convolutional Layer (AGCL) to refine
the graphs and accommodate the variance in appearance. The TAA-GCN outperforms
the state-of-the-art methods on four public benchmarks, UTKFace, MORPHII, CACD,
and FG-NET. Moreover, the TAA-GCN showed reliability in different camera
viewpoints and reduced quality images.