  This work addresses the task of long-term person re-identification.
Typically, person re-identification assumes that people do not change their
clothes, which limits its applications to short-term scenarios. To overcome
this limitation, we investigate long-term person re-identification, which
considers both clothes-changing and clothes-consistent scenarios. In this
paper, we propose a novel framework that effectively learns and utilizes both
global and local information. The proposed framework consists of three streams:
global, local body part, and head streams. The global and head streams encode
identity-relevant information from an entire image and a cropped image of the
head region, respectively. Both streams encode the most distinct, less
distinct, and average features using the combinations of adversarial erasing,
max pooling, and average pooling. The local body part stream extracts
identity-related information for each body part, allowing it to be compared
with the same body part from another image. Since body part annotations are not
available in re-identification datasets, pseudo-labels are generated using
clustering. These labels are then utilized to train a body part segmentation
head in the local body part stream. The proposed framework is trained by
backpropagating the weighted summation of the identity classification loss, the
pair-based loss, and the pseudo body part segmentation loss. To demonstrate the
effectiveness of the proposed method, we conducted experiments on three
publicly available datasets (Celeb-reID, PRCC, and VC-Clothes). The
experimental results demonstrate that the proposed method outperforms the
previous state-of-the-art method.
