  Cloth changing person re-identification(Re-ID) can work under more
complicated scenarios with higher security than normal Re-ID and biometric
techniques and is therefore extremely valuable in applications. Meanwhile,
higher flexibility in appearance always leads to more similar-looking confusing
images, which is the weakness of the widely used retrieval methods. In this
work, we shed light on how to handle these similar images. Specifically, we
propose a novel retrieval-verification framework. Given an image, the retrieval
module can search for similar images quickly. Our proposed verification network
will then compare the input image and the candidate images by contrasting those
local details and give a similarity score. An innovative ranking strategy is
also introduced to take a good balance between retrieval and verification
results. Comprehensive experiments are conducted to show the effectiveness of
our framework and its capability in improving the state-of-the-art methods
remarkably on both synthetic and realistic datasets.
