Abstract
This paper describes a joint intensity metric learning
method to improve the robustness of gait recognition with
silhouette-based descriptors such as gait energy images.
Because existing methods often use the difference of image intensities between a matching pair (e.g., the absolute difference of gait energies for the ?1-norm) to measure a dissimilarity, large intrasubject differences derived
from covariate conditions (e.g., large gait energies caused
by carried objects vs. small gait energies caused by the
background), may wash out subtle intersubject differences
(e.g., the difference of middle-level gait energies derived
from motion differences). We therefore introduce a metric
on joint intensity to mitigate the large intrasubject differences as well as leverage the subtle intersubject differences.
More specifically, we formulate the joint intensity and spatial metric learning in a unified framework and alternately
optimize it by linear or ranking support vector machines.
Experiments using the OU-ISIR treadmill data set B with
the largest clothing variation and large population data set
with bag, ? version containing carrying status in the wild
demonstrate the effectiveness of the proposed method