  This paper investigates the dependence of existing state-of-the-art person
re-identification models on the presence and visibility of human faces. We
apply a face detection and blurring algorithm to create anonymized versions of
several popular person re-identification datasets including Market1501,
DukeMTMC-reID, CUHK03, Viper, and Airport. Using a cross-section of existing
state-of-the-art models that range in accuracy and computational efficiency, we
evaluate the effect of this anonymization on re-identification performance
using standard metrics. Perhaps surprisingly, the effect on mAP is very small,
and accuracy is recovered by simply training on the anonymized versions of the
data rather than the original data. These findings are consistent across
multiple models and datasets. These results indicate that datasets can be
safely anonymized by blurring faces without significantly impacting the
performance of person reidentification systems, and may allow for the release
of new richer re-identification datasets where previously there were privacy or
data protection concerns.
