Abstract:
Object classification is plagued by the issue of session
variation. Session variation describes any variation that
makes one instance of an object look different to another,
for instance due to pose or illumination variation. Recent
work in the challenging task of face verification has shown
that session variability modelling provides a mechanism to
overcome some of these limitations. However, for computer
vision purposes, it has only been applied in the limited setting
of face verification.
In this paper we propose a local region based intersession
variability (ISV) modelling approach, and apply it
to challenging real-world data. We propose a region based
session variability modelling approach so that local session
variations can be modelled, termed Local ISV. We then
demonstrate the efficacy of this technique on a challenging
real-world fish image database which includes images taken
underwater, providing significant real-world session variations.
This Local ISV approach provides a relative performance
improvement of, on average, 23% on the challenging
MOBIO, Multi-PIE and SCface face databases. It also provides
a relative performance improvement of 35% on our
challenging fish image dataset.
