Method for extracting finger vein grain by using directional filtering technique

Abstract

The invention provides a method for extracting finger vein grain by using a directional filtering technique, comprising the steps of positioning finger regions, strengthening filtering and extracting a finger vein mode. The method concretely comprises the following steps: firstly, dividing finger regions on a read-in vein image by using a Kapur entropy threshold value method; secondly, removing burrs for the finger regions by adopting an opening operation in mathematical morphology; thirdly, acquiring a directional diagram of the finger vein region and designing a directional filter by combining the characteristic of the vein grain; fourthly, carrying out filtering enhancement on the image by combining the acquired directional diagram and the directional filter; and finally, carrying outa binaryzaiton operation to extract the finger vein mode by adopting a NiBlack method. The finger vein grain extracted by the method has good connectivity and smoothness and less noise.

Description

(1) Technical field [0001] The invention belongs to the technical field of biological feature identification, and in particular relates to a method for extracting finger vein lines using directional filtering technology. (2) Background technology [0002] Vein recognition is a highly reliable identification method, and its recognition performance is largely related to the quality of vein target extraction. The purpose of vein target extraction is to extract the vein pattern from the background. The extraction effect will directly affect the processing results of feature extraction and feature matching. Therefore, the pros and cons of this part of the algorithm will have a significant impact on the performance of the entire system. . [0003] The venous vessels generally have a network structure, and their ridges have a certain width. These characteristics of the vein image determine the particularity of its extraction method. Traditional vein image extraction techniques ar...

AI Technical Summary

Problems solved by technology

Applying traditional single-threshold segmentation methods such as fixed threshold, overall mean, and overall OSTU to process vein images, it is difficult to obtain ideal segmentation results; however, multi-threshold methods such as local mean and local OSTU have improved threshold segmentation results. , but still cannot achieve satisfactory results

NiBlack [1] The method is currently the best segmentation method in the field of finger vein recognition. It operates on each pixel in the image. It is a local dynamic threshold algorithm. The vein pattern extracted by this method has good connectivity, but from The connectivity of the vein pattern extracted from the low-quality image is not very good, and there is also the phenomenon of over-segmentation

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