  Person re-identification aims to match a person's identity across multiple
camera streams. Deep neural networks have been successfully applied to the
challenging person re-identification task. One remarkable bottleneck is that
the existing deep models are data hungry and require large amounts of labeled
training data. Acquiring manual annotations for pedestrian identity matchings
in large-scale surveillance camera installations is a highly cumbersome task.
Here, we propose the first semi-supervised approach that performs
pseudo-labeling by considering complex relationships between unlabeled and
labeled training samples in the feature space. Our approach first approximates
the actual data manifold by learning a generative model via adversarial
training. Given the trained model, data augmentation can be performed by
generating new synthetic data samples which are unlabeled. An open research
problem is how to effectively use this additional data for improved feature
learning. To this end, this work proposes a novel Feature Affinity based
Pseudo-Labeling (FAPL) approach with two possible label encodings under a
unified setting. Our approach measures the affinity of unlabeled samples with
the underlying clusters of labeled data samples using the intermediate feature
representations from deep networks. FAPL trains with the joint supervision of
cross-entropy loss together with a center regularization term, which not only
ensures discriminative feature representation learning but also simultaneously
predicts pseudo-labels for unlabeled data. Our extensive experiments on two
standard large-scale datasets, Market-1501 and DukeMTMC-reID, demonstrate
significant performance boosts over closely related competitors and outperforms
state-of-the-art person re-identification techniques in most cases.
