  In this paper we introduce a method to overcome one of the main challenges of
person re-identification in multi-camera networks, namely cross-view appearance
changes. The proposed solution addresses the extreme variability of person
appearance in different camera views by exploiting multiple feature
representations. For each feature, Kernel Canonical Correlation Analysis (KCCA)
with different kernels is exploited to learn several projection spaces in which
the appearance correlation between samples of the same person observed from
different cameras is maximized. An iterative logistic regression is finally
used to select and weigh the contributions of each feature projections and
perform the matching between the two views. Experimental evaluation shows that
the proposed solution obtains comparable performance on VIPeR and PRID 450s
datasets and improves on PRID and CUHK01 datasets with respect to the state of
the art.
