Segmenting method of fingerprint image

Abstract

The invention discloses a segmenting method of a fingerprint image. The segmenting method specifically comprises the following steps: a, reading in the fingerprint image; b, quickly cutting the fingerprint image; c, transforming in reverse colour; d, equalizing; e, carrying out top-hat transformation; f, calculating the characteristic quantity in blockst; g, segmenting in blocks; h, processing the image according to morphology; and i, obtaining the segmented fingerprint image. The segmenting method of the finger image is applicable to the fingerprint image poorer in quality, can correctly segment the fingerprint, and is higher in reliability; the ISODATA (Iterative Self-organizing Data Analysis Techniques Algorithm) clustering algorithm is adopted and used for segmenting in blocks, and the clustering speed is higher; the quick cutting way is carried out, thereby the processing time is reduced; the block segmenting mode and pixel segmenting mode are combined, thus the segmented fingerprint is relatively smooth in contour; and the image equalizing mode and the top-hat transformation mode are adopted to enhance the image, and as a result, the segmenting is more efficient.

Description

technical field [0001] The invention relates to a fingerprint image preprocessing method, in particular to a fingerprint image segmentation method. Background technique [0002] Fingerprint image recognition is the earliest application and the cheapest branch of human biometric identification technology. It is widely used in criminal investigation, housing security, identity confirmation of banks, securities, insurance and other financial institutions, access control in important areas, staff or Membership management and other fields have broad application prospects. [0003] Fingerprint image segmentation is an important step in the preprocessing stage of fingerprint recognition. The main purpose is to remove non-fingerprint areas and fingerprint areas that are more noisy and difficult to distinguish. Effective segmentation can reduce the time of subsequent processing, reduce the extraction of false feature points, and improve the accuracy of identification. Rate. [0004...

AI Technical Summary

Problems solved by technology

[0004] At present, the commonly used fingerprint segmentation methods are: a. According to the segmentation method of image grayscale characteristics, the fingerprint image is segmented by using the average value and variance of the fingerprint image grayscale. There are global threshold segmentation and adaptive threshold segmentation. The global threshold segmentation depends on the image grayscale. The bimodal nature of the degree distribution is good. If the bimodality is not obvious or the gray level is multimodal, the segmentation effect is not ideal. The adaptive threshold segmentation will segment the easy-to-restore areas with low contrast and strong directionality, and the segmentation There is block effect in the final fingerprint image; b. According to the segmentation method of fingerprint image direction and frequency characteristics, this method is more complicated, especially the calculation of point direction or point frequency. Difficult to calculate accurately; c According to the segmentation method of grayscale characteristics and directional characteristics, using the local standard deviation (or variance) and consistency features of the image, the linear classification method is used for segmentation. This method takes the directional characteristics and grayscale characteristics into account. Fusion of multiple characteristics, but the selection of its coefficients is very critical, and the setting of the threshold is difficult; d segmentation method based on hidden Markov model; e segmentation method based on transformation and energy field; method d and method e consider Many factors, but the calculation complexity of the algorithm is large, the processing efficiency of low-quality fingerprint images is low, and it cannot be accurately segmented

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