  This paper addresses the problem of matching pedestrians across multiple
camera views, known as person re-identification. Variations in lighting
conditions, environment and pose changes across camera views make
re-identification a challenging problem. Previous methods address these
challenges by designing specific features or by learning a distance function.
We propose a hierarchical feature learning framework that learns invariant
representations from labeled image pairs. A mapping is learned such that the
extracted features are invariant for images belonging to same individual across
views. To learn robust representations and to achieve better generalization to
unseen data, the system has to be trained with a large amount of data.
Critically, most of the person re-identification datasets are small. Manually
augmenting the dataset by partial corruption of input data introduces
additional computational burden as it requires several training epochs to
converge. We propose a hierarchical network which incorporates a
marginalization technique that can reap the benefits of training on large
datasets without explicit augmentation. We compare our approach with several
baseline algorithms as well as popular linear and non-linear metric learning
algorithms and demonstrate improved performance on challenging publicly
available datasets, VIPeR, CUHK01, CAVIAR4REID and iLIDS. Our approach also
achieves the stateof-the-art results on these datasets.
