Abstract. 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. Diferent from conventional (closedworld) 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 nontarget 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 veriication. The framework we proposed is called the adversarial open-world
person re-identiication, 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
veriication. While open-world person re-id is challenging, we show for
the irst time that the adversarial-based approach helps stabilize person
re-id system under imposter attack more efectively