Fingerprint image background segmentation method

Abstract

The invention relates to a fingerprint image background segmentation method, comprising the following steps: step 1, coarsely extracting a fingerprint information boundary value according to difference sum of inter-frame image blocks of adjacent fingerprint sequences; step 2, searching for a stable boundary point according to a difference absolute value of the boundary values of two adjacent points; step 3, processing abnormal boundary values according to the difference absolute value of the adjacent boundary points and a deviation position locating method of the adjacent boundary points; step 4, smoothening the boundary values according to a fingerprint boundary deviation direction; step 5, according to left and right boundary values, substituting a fingerprint image background, thereby making the fingerprint image background more cleaner. The fingerprint image background segmentation method provided by the invention is simple, has fast arithmetic speed and can be realized in a universal ARM chip; and the method is more accurate in segmentation for low-quality fingerprint images and the fingerprint images with mutational background, has wide adaptive range, and can be used for the fingerprint images acquired by a sliding type fingerprint sensor, a planar type fingerprint sensor, and so on.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for segmenting the background of a fingerprint image. Background technique [0002] With the popularization of fingerprint identification applications, high-performance fingerprint identification systems need to extract fingerprint feature information and fingerprint matching algorithms accurately and quickly. However, due to various reasons, most existing collectors usually introduce noise during the fingerprint recording process, resulting in unclean fingerprint background. The noise introduced will seriously affect the later image processing, detail point extraction, etc., thus affecting the fingerprint application effect. In order to improve the accuracy of fingerprint feature extraction, background segmentation is usually performed on the fingerprint image first to remove information that does not contain real lines, so that fingerprint feature...

AI Technical Summary

Problems solved by technology

The global threshold segmentation depends on the bimodal characteristics of the image distribution. If the bimodal effect is not obvious or the distribution is multimodal, the segmentation effect is not ideal.

Adaptive threshold distribution will easily segment areas with low contrast and strong directionality, but adaptive segmentation often has block effects; b. Most other segmentation algorithms are based on the methods of direction field, frequency field, and energy field. Complex, low-efficiency processing of low-quality fingerprint images, and can only be used for DSP devices for processing, resulting in the failure of general ARM chips and ASICs

[0004] In the prior art, segmentation is based on image grayscale characteristics, which has low adaptability to fingerprint image quality or background mutations, and has the defect of inaccurate segmentation of fingerprint images with poor quality or fingerprint images with sudden background mutations.

Other fingerprint image background segmentation algorithms are too complicated, and hyperfunctions are often used, which makes it very difficult for general ARM chips and ASICs to implement

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