  The fact that every human has a distinctive walking style has prompted a
proposal to use gait recognition as an identification criterion. Using
end-to-end learning, I investigated whether the center-of-pressure trajectory
is sufficiently unique to identify a person with a high certainty. Thirty-six
adults walked on a treadmill equipped with a force platform that recorded the
positions of the center of pressure. The raw two-dimensional signals were
sliced into segments of two gait cycles. A set of 20,250 segments from 30
subjects was used to configure and train convolutional neural networks (CNNs).
The best CNN classified a separate set containing 2,250 segments with 99.9%
overall accuracy. A second set of 4,500 segments from the six remaining
subjects was then used for transfer learning. Several small subsamples of this
set were selected randomly and used for fine tuning. Training with two segments
per subject was sufficient to achieve 100% accuracy. The results suggest that
every person produces a unique trajectory of underfoot pressures and that CNNs
can learn the distinctive features of these trajectories. Using transfer
learning, a few strides could be sufficient to learn and identify new gaits.
