Abstract:
Gait is the walking posture and dynamics of a person and is considered a unique
biometric of a person. Based on this biometric and using various algorithms one can
recognize the person’s identity with high precision. Although many conventional machine
learning and deep learning methods that learn a person’s identity based on their
gait have been proposed, there still exist some typical limitations. While conventional
machine learning methods are error-prone to the background noise, more advanced
methods based on CNN models do not well capture the temporal dependencies in
terms of inter-frame correlation for gait recognition. This work addresses the limitations
of previous works by employing deep sequential models and demonstrating
their effectiveness and efficiency in learning gait information using 3D human skeletal
data. We propose a person identification model, that uses both the joint coordinates
and features derived from them, including joint distance, joint orientation, and joint
velocity. Sequential models based on Long Short-Term Memory and Transformer networks
capture the spatial correlations of skeletal joints within a single frame and the
temporal dependencies and dynamics of the joints throughout a sequence of frames.
The effectiveness and efficiency of the proposed methods, the impact of data augmentation
methods, a combination of derived gait features were studied and analyzed.
The experimental results show that the proposed models achieve high person identification
accuracy on the UPCV Gait (98.26%), and KS20 VisLab Multi-View (90.86%)
datasets, which are competitive to the previous state-of-the-art methods.