The gait of a person is a very attractive biometric as it can be captured from a distance,
without the subject's cooperation. However, there is a large number of confounding
factors that may dramatically affect its performance. The main objective of this work
is to set a path towards full invariance in gait recognition. While recent deep learning
techniques have shown promising results in tackling similar problems, they require large
amounts of, often labelled, data. Having this in mind, a new multi-modal gait dataset is
introduced, comprised of more than 6.5 million frames of real 3D human motion capture
data and 2D image data. Based on this dataset, a data augmentation methodology is also
introduced that provides the tools to synthetically generate data, while simultaneously
controlling several different confounding factors.
The way the data were collected and prepared, to introduce the augmentation methodology,
provided the opportunity to answer a fundamental question regarding the source
of identity information in gait recognition. This is the first attempt to answer this
question. Statistically significant results suggest that the dynamics of the individual's
motion is the main source of identity information in the most widely used features for
gait recognition techniques.
The potential of 3D volumetric gait information was also investigated using a Convolutional
Deep Belief Network. The trained model was able to estimate the phase during a
gait cycle, given a static 3D body volume. The ability of the model to learn high-level
representations of 3D body shapes was also validated through an additional experiment,
in which the full 3D body shape was successfully filled-in, given a 2.5D visible surface of
it. The model was also used for gait recognition, using the collected 3D volumetric data,
while introducing invariance to certain data capture conditions. The high-level features
that the network learned were visualised in an informative way, showing the ability of
the model to capture high-level representations of 3D motion patterns.