Gait recognition via deep learning of the center-of-pressure trajectory: A proof-of-concept study for biometric applications

Each human being has a distinctive style of walking. Gait recognition has therefore been proposed for identification purpose. Using end-to-end learning, I investigated whether the center-of-pressure trajectory on the ground was sufficiently unique to identify a person with a high certainty. 36 adults walked for 30min on a treadmill instrumented with a force platform that recorded the position of the center of pressure at 50Hz. The raw 2D signals were sliced into segments of two gait cycles. A balanced set of 20,250 segments from 30 subjects was used to configure and train convolutional neural networks (CNN). The best CNN classified a separated set containing 2,250 segments with 99.9% overall accuracy. A second set of 4,500 segments from the 6 remaining subjects was then used for transfer learning. Several small subsamples of this second set were randomly selected and used to fine-tune the top layers of the CNN. Training with 12 segments (2 per subject) was enough to reach 100% accuracy. The results suggest that everyone produces a unique trajectory of underfoot pressures. CNNs can learn the distinctive features of these trajectories. Using transfer learning, few strides could be enough to learn and identify new gaits.