  In this paper, we propose a graph correspondence transfer (GCT) approach for
person re-identification. Unlike existing methods, the GCT model formulates
person re-identification as an off-line graph matching and on-line
correspondence transferring problem. In specific, during training, the GCT
model aims to learn off-line a set of correspondence templates from positive
training pairs with various pose-pair configurations via patch-wise graph
matching. During testing, for each pair of test samples, we select a few
training pairs with the most similar pose-pair configurations as references,
and transfer the correspondences of these references to test pair for feature
distance calculation. The matching score is derived by aggregating distances
from different references. For each probe image, the gallery image with the
highest matching score is the re-identifying result. Compared to existing
algorithms, our GCT can handle spatial misalignment caused by large variations
in view angles and human poses owing to the benefits of patch-wise graph
matching. Extensive experiments on five benchmarks including VIPeR, Road,
PRID450S, 3DPES and CUHK01 evidence the superior performance of GCT model over
other state-of-the-art methods.
