Covariate factor mitigation techniques for robust human gait recognition
Imagining trying to recognise a person from CCTV images. Traditional biometrics
such as facial recognition is rendered ineffective should a suspect's face be
concealed. Gait can identify a person based on their unique walking manner and
posture. As a biometric, gait is favourable as the capture requires no cooperation,
consent or contact from the suspect.
Before gait recognition can be deployed during real world surveillance, robustness
to common real world covariate factors must be established during controlled validation
datasets. Our research focuses on boosting the robustness to covariate factors which
affect the natural gait appearance and motion; examples include clothing, shoes,
carrying a bag, elapsed time between capture (months) and complex couples thereof.
Our most recent work exploits the novel exploitation of popular single compact 2D gait
representations and model-free skeleton-based representations to yield the Skeleton
Variance Image (SVIM). Through space- and time-normalisation, a gait sequence is
condensed into a single compact 2D gait representations which is highly beneficial for
memory and computational processing costs. Skeleton-based representations are sparsely
employed in gait recognition due to the inherent boundary perturbation sensitivity occurring
from the imperfect extraction of silhouettes. By employing a smooth distance function generated
by the Poisson equation, we generate robust skeletons capable of differentiating between the
natural gait and covariate factors - overcoming a significant limitation of gait recognition.
To further combat the effects of covariate factors, motion features are extracted for their
saliency and consistency over time compared to their appearance feature counterparts.
Our robust gait recognition approach achieves state of the art results when validated on
the standardised publicly available TUM GAID dataset, shown below:
Publications:
- Dynamic Distance-Based Shape Features for Gait Recognition, T. Whytock, A. Belyaev, N.M. Robertson, Journal of Mathematical Imaging and Vision, Volume 50, Issue 3 , pp 314-326, Journal of Mathematical Imaging and Vision, Springer, 2014. doi 10.1007/s10851-014-0501-8
- Robust Gait Recognition Via Covariate Factor Mitigation, International Conference on Imaging for Crime Detection and Prevention, 2013. doi 10.1049/ic.2013.0275
- Towards Robust Gait Recognition T. Whytock, A. Belyaev, N.M. Robertson, Advances in Visual Computing, Lecture Notes in Computer Science, Part II, Volume 8034, pp 523-531, 2013 (Presented at the International Symposium on Visual Computing). doi 10.1007/978-3-642-41939-3_51
