Fingerprint Entropy and Identification Capacity Estimation Based on Pixel-Level Generative Modelling

摘要

A family of texture-based generative models for fingerprint images is proposed. The generative models are used to estimate upper bounds on the image entropy for systems with small sensor acquisition. The identification capacity of such systems is then estimated using the mutual information between different samples from the same finger. Similar to the generative model for entropy estimation, pixel-level model families are proposed for estimating the similarity between fingerprint images with a given global affine transformation. These models are used for mutual information estimation, and are also adopted to compensate for local deformations between samples. Finally, it is shown that sensor sizes as small as 52 x 52 pixels are potentially sufficient to discriminate populations as large as the entire world population that ever lived, given that the complexity-unconstrained recognition algorithm is available which operates on the lowest possible pixel level.