Method and system for recognizing dorsal hand vein based on in-bit-plane block mutual information

A vein recognition and mutual information technology, applied in biometrics recognition, character and pattern recognition, subcutaneous biometrics, etc., can solve the problems of low robustness, low image distortion robustness, etc., and achieve intra-class correlation High, improved recognition rate, high robustness effect

Active Publication Date: 2019-01-08
NORTH CHINA UNIVERSITY OF TECHNOLOGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The technical effect described by this patented technology allows us to recognize fingerprints from images without being affected with ambient lighting conditions or other factors like background noise. By selecting specific bits within each pixel's area instead of just looking at them separately, we can achieve better results than traditional methods.

Problems solved by technology

This patents describes three technical ways to identify individuals from their entire life without being too much attached themselves (finger prints). These techniques include traditional approaches like optical character analysis and machine learning algorithms. They offer several benefits over existing technologies including those related to biometry, finger printings, facial scans, and sensory input devices. Overall, these advancements provide better safety, convenience, affordability, accuracy, flexibility, durability, immunity against forgracy attacks, ease of collection, storage, and production costs.

Method used

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  • Method and system for recognizing dorsal hand vein based on in-bit-plane block mutual information
  • Method and system for recognizing dorsal hand vein based on in-bit-plane block mutual information
  • Method and system for recognizing dorsal hand vein based on in-bit-plane block mutual information

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Embodiment 1

[0062] Embodiment 1 of the present invention provides a hand vein recognition method based on block mutual information in a bit plane, such as figure 1 As shown, the identification method includes:

[0063] S1: Obtain the region of interest in the dorsal hand vein image, such as Figure 3-2 As shown in , where the image of the dorsal veins of the hand is collected by the hardware image acquisition device, the schematic diagram is shown in figure 2 ; The region of interest in the dorsal hand vein image is obtained through the centroid adaptive method, such as Figure 3-1 As shown, the specific method is: according to the formula Obtain the centroid of the dorsal hand vein image O(x 0 ,y 0 ), and take the center of mass as the center of the largest inscribed circle in the area of ​​interest of the dorsal hand vein image, and use the diameter of the largest inscribed circle as the standard for size normalization, and after size normalization, intercept an area with a size of e

Embodiment 2

[0078] Embodiment 2 of the present invention provides a hand vein recognition system based on block mutual information in the bit plane, such as Image 6 As shown, the identification system includes:

[0079] The image preprocessing module 1 is used to obtain the region of interest of the dorsal hand vein image, wherein the dorsal hand vein image is collected by a hardware image acquisition device, and the region of interest of the dorsal hand vein image is obtained by a centroid adaptive method; for the obtained dorsal hand vein image The region of interest is subjected to grayscale normalization processing until the pixel value of each pixel is in the range of 0-255 to obtain a grayscale image. In order to obtain the outline of the veins on the back of the hand, a gradient-enhanced vein image segmentation method is used to obtain a sense of the vein image on the back of the hand. Segment the region of interest, obtain the segmented binary image, and multiply the binary image wi

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Abstract

The invention provides a method and a system for recognizing dorsal hand vein based on in-bit-plane block mutual information. The method and the system generate eight bit-plane maps from gray-scale images retaining the dorsal hand vein contour, and extract the mutual information of the blocks in the bit-plane maps as features, so that the method has high robustness to image distortion and the like, high intra-class correlation and low inter-class correlation of the features, good separability, and high recognition rate.

Description

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Claims

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Application Information

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Owner NORTH CHINA UNIVERSITY OF TECHNOLOGY
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