  Partial person re-identification (ReID) is a challenging task because only
partial information of person images is available for matching target persons.
Few studies, especially on deep learning, have focused on matching partial
person images with holistic person images. This study presents a novel deep
partial ReID framework based on pairwise spatial transformer networks
(STNReID), which can be trained on existing holistic person datasets. STNReID
includes a spatial transformer network (STN) module and a ReID module. The STN
module samples an affined image (a semantically corresponding patch) from the
holistic image to match the partial image. The ReID module extracts the
features of the holistic, partial, and affined images. Competition (or
confrontation) is observed between the STN module and the ReID module, and
two-stage training is applied to acquire a strong STNReID for partial ReID.
Experimental results show that our STNReID obtains 66.7% and 54.6% rank-1
accuracies on partial ReID and partial iLIDS datasets, respectively. These
values are at par with those obtained with state-of-the-art methods.
