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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 biomeĢtrie identification. In this paper, we review the role of sparse representation and CS for efficient biomeĢtrie identification. Algorithms to perform identification from face and iris data are reviewed. By applying Random Projections it is possible to purposively hide the biomeĢtrie data within a template. This procedure can be effectively employed for securing and protecting personal biomeĢtrie data against theft. Some of the most compelling challenges and issues that confront research in biometrics using sparse representations and CS are also addressed