Sparse Representations and Random Projections for Robust and Cancelable Biometrics

In recent years, the theories of Sparse Representation (SR) and Compressed Sensing (CS) have emerged as powerful tools for efficiently processing data in non-traditional ways. An area of promise for these theories is biométrie identification. In this paper, we review the role of sparse representation and CS for efficient biométrie identification. Algorithms to perform identification from face and iris data are reviewed. By applying Random Projections it is possible to purposively hide the biométrie data within a template. This procedure can be effectively employed for securing and protecting personal biométrie data against theft. Some of the most compelling challenges and issues that confront research in biometrics using sparse representations and CS are also addressed.