  Gait is a popular biometric pattern used for identifying people based on
their way of walking. Traditionally, gait recognition approaches based on deep
learning are trained using the whole training dataset. In fact, if new data
(classes, view-points, walking conditions, etc.) need to be included, it is
necessary to re-train again the model with old and new data samples.
  In this paper, we propose iLGaCo, the first incremental learning approach of
covariate factors for gait recognition, where the deep model can be updated
with new information without re-training it from scratch by using the whole
dataset. Instead, our approach performs a shorter training process with the new
data and a small subset of previous samples. This way, our model learns new
information while retaining previous knowledge.
  We evaluate iLGaCo on CASIA-B dataset in two incremental ways: adding new
view-points and adding new walking conditions. In both cases, our results are
close to the classical `training-from-scratch' approach, obtaining a marginal
drop in accuracy ranging from 0.2% to 1.2%, what shows the efficacy of our
approach. In addition, the comparison of iLGaCo with other incremental learning
methods, such as LwF and iCarl, shows a significant improvement in accuracy,
between 6% and 15% depending on the experiment.
