  Visual perception of a person is easily influenced by many factors such as
camera parameters, pose and viewpoint variations. These variations make person
Re-Identification (ReID) a challenging problem. Nevertheless, human attributes
usually stand as robust visual properties to such variations. In this paper, we
propose a new method to leverage features from human attributes for person
ReID. Our model uses a tensor to non-linearly fuse identity and attribute
features, and then forces the parameters of the tensor in the loss function to
generate discriminative fused features for ReID. Since tensor-based methods
usually contain a large number of parameters, training all of these parameters
becomes very slow, and the chance of overfitting increases as well. To address
this issue, we propose two new techniques based on Structural Sparsity Learning
(SSL) and Tensor Decomposition (TD) methods to create an accurate and stable
learning problem. We conducted experiments on several standard pedestrian
datasets, and experimental results indicate that our tensor-based approach
significantly improves person ReID baselines and also outperforms state of the
art methods.
