  Deep learning techniques have recently been utilized for model-free
age-associated gait feature extraction. However, acquiring model-free gait
demands accurate pre-processing such as background subtraction, which is
non-trivial in unconstrained environments. On the other hand, model-based gait
can be obtained without background subtraction and is less affected by
covariates. For model-based gait-based age group classification problems,
present works rely solely on handcrafted features, where feature extraction is
tedious and requires domain expertise. This paper proposes a deep learning
approach to extract age-associated features from model-based gait for age group
classification. Specifically, we first develop an unconstrained gait dataset
called Multimedia University Gait Age and Gender dataset (MMU GAG). Next, the
body joint coordinates are determined via pose estimation algorithms and
represented as compact gait graphs via a novel part aggregation scheme. Then, a
Part-AdaptIve Residual Graph Convolutional Neural Network (PairGCN) is designed
for age-associated feature learning. Experiments suggest that PairGCN features
are far more informative than handcrafted features, yielding up to 99% accuracy
for classifying subjects as a child, adult, or senior in the MMU GAG dataset.
