Chapter 17: Applications of Bloom Filter in biometrics
Abstract
Biometric computation includes feature extraction from the biometric images, the transformation of original data into a data compressed form, and fast authentication of query biometric images. However, biometric computation is compute-intensive. Sophisticated techniques are required for feature extraction. The biometric data requires huge memory for storage. Comparison between queried biometric image and saved biometric sample has high time complexity. Bloom Filter, a probabilistic data structure, is a simple solution for some of the issues of biometrics. Bloom Filter transforms the original data into a highly compressed data form which requires low memory. Bloom Filter is also an ...
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