  We present a novel framework to exploit privileged information for
recognition which is provided only during the training phase. Here, we focus on
recognition task where images are provided as the main view and soft biometric
traits (attributes) are provided as the privileged data (only available during
training phase). We demonstrate that more discriminative feature space can be
learned by enforcing a deep network to adjust adaptive margins between classes
utilizing attributes. This tight constraint also effectively reduces the class
imbalance inherent in the local data neighborhood, thus carving more balanced
class boundaries locally and using feature space more efficiently. Extensive
experiments are performed on five different datasets and the results show the
superiority of our method compared to the state-of-the-art models in both tasks
of face recognition and person re-identification.
