Biometric identification often involves explicit comparison of a probe against each template stored in a database. This process becomes extremely time-consuming as the size of the database increases. Filtering approaches use a light weight comparison to select a smaller set of candidate templates from the database 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 work, we

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... xplore the effectiveness of weak features in a cascade for filtering fingerprint databases. We start with a set of potential indexing features computed from minutiae triplets and minutiae quadruplets. Each stage of filtering consists of projecting the probe onto a specific line and the removal of database samples outside a window around the probe. The critical problem in this process is the selection of lines for projection at each stage of the filtering. We show that by using a set of random lines and the proposed fitness function, one can achieve better results that optimization methods such as PCA or LDA. Experimental results show that using an ensemble of projections we can reduce the penetration to 26% at a hit rate of 99%. As each stage of the cascade is extremely fast, and filtering is progressive along the cascade, one can terminate the cascade at any point to achieve the desired performance. One can also combine this method with other indexing methods to improve the overall accuracy and speed. We present detailed experimental results on various aspects of the process on the FVC 2002 dataset.