Immune cloned finger venous image characteristic extraction method based on linear weighted function

Abstract

The invention discloses an immune cloned finger venous image characteristic extraction method based on a linear weighted function (LWF), aiming at the problems that noise is easily misjudged into venous information and the worse venous information is misjudged into the noise for characteristic extraction in a low-quality venous image. In an algorithm of the method, an initial antibody is generated by adopting a self-adaptive threshold method, a weight value is obtained by adopting a curve fitting mode, and balancing denoising and enhancing boundaries are achieved by carrying out linear weighting on a venous region. An affinity function and an antibody concentration function are constructed, and the growth of the venous information is promoted and the interference of the noise is inhibitedaccording to the affinity function and an antibody concentration value. Simulation results indicate that compared with other algorithms, the algorithm has the characteristics of strong authenticity, accuracy, continuity, more abundant detail characteristics and the like and particularly has strong noise resisting capability and good extraction effect for a low-quality venous image.

Description

technical field [0001] The invention relates to feature extraction technology of finger vein pictures. Background technique [0002] In today's society, biometric identification technology has attracted more and more attention. The diversity of biological characteristics has resulted in the diversity of identification technologies, and vein feature recognition technology, as one of them, has also emerged as the times require. In 2000, M.Kono and other researchers developed a finger vein near-infrared recognition system for personal identification with the support of Hitachi Corporation, Japan, and applied it to personal identification. For different biometric feature recognition, it is necessary to adopt a suitable feature extraction algorithm. The characteristics of finger veins are no exception. Because the veins of different people have different sizes, the characteristics of small veins are not easy to extract; in addition, the finger vein images obtained by the curren...

AI Technical Summary

Problems solved by technology

The characteristics of finger veins are no exception. Because the veins of different people have different sizes, the characteristics of small veins are not easy to extract; in addition, the finger vein images obtained by the current acquisition device have indistinct distinction between the target and the background, noise and vein information. Coexistence, it is quite difficult to obtain accurate vein information from it

Moreover, the feature extraction of low-quality vein images is easy to misjudge noise as vein information, and misjudge poor vein information as noise

In addition, due to the influence of environment and temperature, the expansion and contraction of finger vein blood vessels also brings troubles to feature extraction, which is a hot topic in current research at home and abroad.

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