  In a typical real-world application of re-id, a watch-list (gallery set) of a
handful of target people (e.g. suspects) to track around a large volume of
non-target people are demanded across camera views, and this is called the
open-world person re-id. Different from conventional (closed-world) person
re-id, a large portion of probe samples are not from target people in the
open-world setting. And, it always happens that a non-target person would look
similar to a target one and therefore would seriously challenge a re-id system.
In this work, we introduce a deep open-world group-based person re-id model
based on adversarial learning to alleviate the attack problem caused by similar
non-target people. The main idea is learning to attack feature extractor on the
target people by using GAN to generate very target-like images (imposters), and
in the meantime the model will make the feature extractor learn to tolerate the
attack by discriminative learning so as to realize group-based verification.
The framework we proposed is called the adversarial open-world person
re-identification, and this is realized by our Adversarial PersonNet (APN) that
jointly learns a generator, a person discriminator, a target discriminator and
a feature extractor, where the feature extractor and target discriminator share
the same weights so as to makes the feature extractor learn to tolerate the
attack by imposters for better group-based verification. While open-world
person re-id is challenging, we show for the first time that the
adversarial-based approach helps stabilize person re-id system under imposter
attack more effectively.
