Robust Face Recognition
In this work, we address the problem of classifying faces under adverse noise conditions. Specifically, we consider extreme occlusions (up to 50%). To make the classification problem tractable, we allow scenarios when multiple instances of the same person's face are presented to the classifier (i.e. a face video). In an effort to mimic realistic classification scenarios, we do not constrain occlusions to be stationary.