  User authentication through gait analysis is a promising application of
discriminative neural networks -- particularly due to the ubiquity of the
primary sources of gait accelerometry, in-pocket cellphones. However,
conventional machine learning models are often too large and computationally
expensive to enable inference on low-resource mobile devices. We propose that
binarized neural networks can act as robust discriminators, maintaining both an
acceptable level of accuracy while also dramatically decreasing memory
requirements, thereby enabling on-device inference. To this end, we propose
BiPedalNet, a compact CNN that nearly matches the state-of-the-art on the
Padova gait dataset, with only 1/32 of the memory overhead.
