  An intrinsic challenge of person re-identification (re-ID) is the annotation
difficulty. This typically means 1) few training samples per identity, and 2)
thus the lack of diversity among the training samples. Consequently, we face
high risk of over-fitting when training the convolutional neural network (CNN),
a state-of-the-art method in person re-ID. To reduce the risk of over-fitting,
this paper proposes a Pseudo Positive Regularization (PPR) method to enrich the
diversity of the training data. Specifically, unlabeled data from an
independent pedestrian database is retrieved using the target training data as
query. A small proportion of these retrieved samples are randomly selected as
the Pseudo Positive samples and added to the target training set for the
supervised CNN training. The addition of Pseudo Positive samples is therefore a
data augmentation method to reduce the risk of over-fitting during CNN
training. We implement our idea in the identification CNN models (i.e.,
CaffeNet, VGGNet-16 and ResNet-50). On CUHK03 and Market-1501 datasets,
experimental results demonstrate that the proposed method consistently improves
the baseline and yields competitive performance to the state-of-the-art person
re-ID methods.
