  Gait is a behavioral biometric modality that can be used to recognize
individuals by the way they walk from a far distance. Most existing gait
recognition approaches rely on either silhouettes or skeletons, while their
joint use is underexplored. Features from silhouettes and skeletons can provide
complementary information for more robust recognition against appearance
changes or pose estimation errors. To exploit the benefits of both silhouette
and skeleton features, we propose a new gait recognition network, referred to
as the GaitPoint+. Our approach models skeleton key points as a 3D point cloud,
and employs a computational complexity-conscious 3D point processing approach
to extract skeleton features, which are then combined with silhouette features
for improved accuracy. Since silhouette- or CNN-based methods already require
considerable amount of computational resources, it is preferable that the key
point learning module is faster and more lightweight. We present a detailed
analysis of the utilization of every human key point after the use of
traditional max-pooling, and show that while elbow and ankle points are used
most commonly, many useful points are discarded by max-pooling. Thus, we
present a method to recycle some of the discarded points by a Recycling
Max-Pooling module, during processing of skeleton point clouds, and achieve
further performance improvement. We provide a comprehensive set of experimental
results showing that (i) incorporating skeleton features obtained by a
point-based 3D point cloud processing approach boosts the performance of three
different state-of-the-art silhouette- and CNN-based baselines; (ii) recycling
the discarded points increases the accuracy further. Ablation studies are also
provided to show the effectiveness and contribution of different components of
our approach.
