Face recognition via sparse representation - Almeida, David Moreira de (2019)
Face recognition has recently seen a peek in interest due to developments in deep learning. These developments incited great attention to the fi eld, not only from the research community, but also from a commercial perspective. While such methods provide the best accuracies when performing face recognition tasks, they often require millions of face images, a substantial amount of processing power and a considerable amount of time to develop. In the recent years, sparse representations have been successfully applied to a number of computer vision applications. One of those applications is face recognition. One of the first methods proposed for this task was the Sparse Representation Based Classi fication (SRC). Since then, several different methods, based on SRC have been proposed. These include dictionary learning based methods, as well as patch based classi fication. This thesis aims to study face recognition using sparse classi fication. Multiple methods will be explored, and some of these will be tested extensively in order to provide a comprehensive view of the fi eld.