Cascaded Filtering for Biometric Identification Using Random Projections
Biometric identification often involves explicit comparison of a probe template against each template stored in a database. This approach becomes extremely time-consuming as the size of the database increases. Filtering approaches use a light-weight comparison to reduce the database to smaller set of candidates for explicit comparison. However, most existing filtering schemes use specific features that are hand-crafted for the biometric trait at each stage of the filtering. In this paper, the authors show that a cascade of simple linear projections on random lines can achieve significant levels of filtering.