Gamma Distribution of FAST Feature for Statistical Image Modelling in Fingerprint Image Classification

Abstract

A unique method for modelling Features from accelerated segment test (FAST) with the Gamma distribution for statistical image data is introduced. Instead of using an image's FAST feature instantly; we design the FAST function to decrease the other global extraction function. The method of moment is used to predict Gamma distribution parameters. FAST's mathematical depiction is the depiction of matrix and is too complicated to be implemented in the ranking of images. We are therefore proposing a fresh statistical function to display the picture in a few dimensional numbers. Our proposed feature utilizes FAST method of Gamma distribution that can be used directly in the identification of fingerprint images. We demonstrate that the Gamma distribution works with FAST and has been effectively implemented in the identification of fingerprint images. To show the benefits of the proposed feature, Shivang Patel Fingerprint Database is used on which classifier evaluation and state-of - the-art comparison are performed.