CN110119724A - A kind of finger vein identification method - Google Patents

A kind of finger vein identification method Download PDF

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Publication number
CN110119724A
CN110119724A CN201910417207.1A CN201910417207A CN110119724A CN 110119724 A CN110119724 A CN 110119724A CN 201910417207 A CN201910417207 A CN 201910417207A CN 110119724 A CN110119724 A CN 110119724A
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Prior art keywords
vein
image
finger
carried out
finger vein
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周卫斌
信振朝
胡阳阳
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Tianjin University of Science and Technology
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Tianjin University of Science and Technology
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Priority to CN201910417207.1A priority Critical patent/CN110119724A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/30—Noise filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/34—Smoothing or thinning of the pattern; Morphological operations; Skeletonisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/14—Vascular patterns

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Human Computer Interaction (AREA)
  • Collating Specific Patterns (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

The present invention relates to a kind of finger vein identification methods, include the following steps: S1, carry out ROI positioning to the image of high-definition camera acquisition；S2, gray scale normalization processing is carried out to image different piece；S3, mean filter, gaussian filtering elimination noise are carried out to image；S4, lines center is judged using curvature measuring；S5, the contrast that enhancing grayscale image is converted by gamma；S6, using judging edge pixel point based on the thinning algorithm of concordance list；S7, extraction is referred to vein skeleton merged, expanded, smoothing processing.By template matching, Hu not bending moment, improved Zernike square, finger hand vein recognition is carried out.Especially under the conditions of Image Acquisition is unsharp, this method has strong anti-interference ability the recognition capability with degree of precision.

Description

A kind of finger vein identification method
Technical field
The invention belongs to image procossing and technical field of biometric identification more particularly to a kind of finger veins.Recognition methods.
Background technique
Refer to that hand vein recognition is a kind of technology with the living things feature recognition personal identification of human body.Bio-identification skill at this stage Art relies primarily on fingerprint recognition and iris recognition, and both belong to the body surface information characteristics of acquisition human body, it is easily stolen take and It imitates.And refer to vein identification technology as characteristics of human body's identification technology of new generation, due to its living body, uniqueness, from approach The case where preventing personal information feature to be stolen.It is also avoided simultaneously because being influenced brought by other environmental factors.
Refer to hand vein recognition equipment in identification process, because core component (light source, optical filter, camera) can not be accurately positioned Cause collected original finger vein image clarity lower；The performance of recognizer be easy by image rotation, translation and The influence of noise.
Therefore, a kind of to be pre-processed for original image, effectively inhibition image rotation, translation brings shadow to accuracy of identification Loud finger hand vein recognition algorithm is urgently developed.
Summary of the invention
The present invention provides a kind of finger vein identification methods.
Refer to that vein identification method mainly includes four parts, image preprocessing refers to that vein pattern extracts generation matching template, refers to The identification of vein algorithm.
Image preprocessing part is by image gray processing, ROI region extraction, linear normalization, mean filter, gaussian filtering etc. Part forms.Using Sobel operator extraction ROI region, grayscale image is denoised by linear filtering, smoothing processing.
Feature extraction is to extract clearly refer to veinprint, skeleton, finally give birth in grayscale image after image preprocessing At enrollment.The method that the present invention uses curvature measuring, detection refer to the extreme point of grey scale change in vein image, from more complex Background in extract finger veinprint；Picture quality is enhanced by Gamma variation later, uses OTSU Threshold segmentation Method, by grayscale image binaryzation,；The method detected by neighborhood, the thicker train of thought after binaryzation is refined, extraction refers to Vein skeleton.Finger vein skeleton is handled finally by the methods of rotation fusion, expansion, generates and is used for matched finger vein Template
Refer to that vein algorithm identification division includes template matching, Hu not bending moment, improved Zernike match by moment three parts.Mould The advantages that plate matching is fast because of its processing speed, the important component as matching algorithm.It, can when Image Acquisition quality is higher Directly to be identified, recognition efficiency is improved.When template matching can not accurately be identified, acquired by comparing calculation Refer to that vein image is identified with the seven rank Hu squares for referring to sample in vein library.Further, refer to changing for vein image by calculating Into seven rank square of Zernike, Hu can be made up in high-order because rotation bring identification is difficult.
Then determine when any one matching degree in three kinds of template matching, Hu square, Zernike square recognition functions is higher than threshold value Successful match.
Compared with prior art, beneficial effects of the present invention are as follows:
The present invention may be implemented to refer to vein high precision collecting, solve in identification process there are image rotation, translation Brought identification is difficult.150 finger vein samples of 50 people are acquired, being rotated up in finger as left and right angle is 30 ° In the case where.Identify situation such as following table.
It is an advantage of the current invention that during referring to hand vein recognition, the posture being put into due to finger is different, illumination not Together.It is difficult to ensure that the effective information that same individual is extracted when referring to the template and matching that acquire when vein typing is consistent.Greatly Majority can all be rotated, be deviated.This brings no small challenge to high-precision identification.Based on this, the invention proposes one kind to change Into Zernike square recognizer, preferably to characterize with this and refer to the global characteristics of veinprint, improve accuracy of identification.
Detailed description of the invention
Fig. 1 is that the present invention refers to vein image feature extraction flow chart
Fig. 2 is that the present invention refers to hand vein recognition algorithm flow chart
Specific embodiment
The present invention provides a kind of identification, accurately human body refers to vein identification method.
A specific embodiment of the invention is described below, so that those skilled in the art understand this hair It is bright.
It is the feature extraction flow chart for referring to one embodiment of hand vein recognition algorithm with reference to Fig. 1, Fig. 1；As shown in figure, should Extracting method includes step S1 to step S7.
In step sl, the finger vein image currently acquired is obtained, by calculating the gradient of grayscale image horizontal direction, to examine The edge in vertical direction is surveyed, by calculating the gradient of grayscale image vertical direction, comes the edge on detection level direction；
In step s 2, the gray value of effective information is more concentrated, and valid interval span is less than 0-255, using formulaCarry out linear normalization processing；
In step s3, using formulaMean filter is carried out, gaussian filtering uses formula
In step s 4, derivation is carried out to the grey scale pixel value of treated image, determination refers to veinprint center；
In step s 5, using formula s=crγGamma map function is carried out to the gray value of image, to image grayscale area Divide enhancing；
In step s 6, refer to the edge of vein binary map by traversing, and go to according to concordance list to judge 8 fields of the point Situation.It is removed if result is 1；It is image edge pixels point if result is 0, needs to retain.
In the step s 7, a pair is referred to that vein skeleton rotates multiple angles, more secondary skeleton images is merged again later. The structural element progress morphological dilations processing that finger vein image after fusion is square using core is calculated again.Generation refers to Vein enrollment.Wherein calculating core is
It is to refer to vein algorithm identification process with reference to Fig. 2, Fig. 2.
Further, template matching formula is T=1- ∫ ∫s(f-t)2Dx dy, wherein S table indicates the definition of image t (x, y) Domain calculates the similarity S for referring to vein input picture and sample image, and 1 indicates similarity highest, and 0 indicates no similarity.
Further, the calculation of Hu square is as follows
φ1=η20+η02
φ2=(η20-η02)2+4η11 2
φ3=(η30-3η12)2+(3η21-η03)2
φ4=(η30+η12)2+(η21+η03)2
φ5=(η30-3η12)(η30+η12)[(η30+η12)2-3(η21+η03)2]+
(3η21-η03)(η21+η03)[3(η30+η12)2-(η21+η03)2]
φ6=(η20-η02)[(η30+η12)2-(η21+η03)2]+
4η11(η30+η12)(η21+η03)
φ7=(3 η21-η03)(η30+η12)[(η30+η12)2-3(η21+η03)2]+
(3η12-η30)(η21+η03)[3(η30+η12)2-(η21+η03)2]
For seven not bending moments of bis- third moment of Hu construction, ηpqFor (p+q) rank not bending moment.
Further, the calculation of improved Zernike square is as follows.
First is that finding out the shape mass center O (x for referring to vein train of thoughto, yo), recycle Euclidean distance to find out in its train of thought apart from shape Shape mass center O farthest pixel B (xb, yb), r is distance between the two, so that it is determined that it refers to the circumscribed circle of vein train of thought, half Diameter is r, and all target points are normalized in unit circle, this Zernike square allowed for has translation and scale not Denaturation；
Second is that calculating 0 rank geometric moment of image middle finger vein train of thought.
m00=∫ ∫ f (x, y) dxdy
Third is that each rank Zernike square in unit of account circle
Fourth is that normalizing Zernike square using 0 rank geometric moment
Further, the various features value for referring to vein image is compared with vein library is referred to, when template matching, seven rank Hu Square value, the matching degree with improved Zernike square value three, are compared with threshold value, and wherein any one value is greater than threshold value then Successful match.

Claims (5)

1. a kind of finger vein identification method, which comprises the steps of:
S1, ROI positioning is carried out to the image of high-definition camera acquisition；
S2, gray scale normalization processing is carried out to image different piece；
S3, mean filter, gaussian filtering elimination noise are carried out to image；
S4, lines center is judged using curvature measuring；
S5, the contrast that enhancing grayscale image is converted by gamma；
S6, using judging edge pixel point based on the thinning algorithm of concordance list；
S7, extraction is referred to vein skeleton merged, expanded, smoothing processing.Pass through template matching, Hu not bending moment, improved Zernike square carries out finger hand vein recognition.
2. finger vein identification method according to claim 1, which is characterized in that use Sobel operator pair in step s 2 Gray level image carries out edge detection.
3. finger vein identification method according to claim 1, which is characterized in that finger vein pattern extraction process mean curvature Detection, power law transformation, the thinning algorithm based on concordance list.
4. finger vein identification method according to claim 1, which is characterized in that improved when carrying out finger hand vein recognition Zernike square.Its calculation method is as follows.
Find out the shape mass center O (x for referring to vein train of thoughto, yo), recycle Euclidean distance to find out in its train of thought apart from shape mass center O most Remote pixel B (xb, yb), r is distance between the two, so that it is determined that it refers to the circumscribed circle of vein train of thought, radius r, institute Some target points normalize in unit circle, this Zernike square allowed for has translation and scale invariance；
Calculate 0 rank geometric moment of image middle finger vein train of thought.
m00=∫ ∫ f (x, y) dxdy
Each rank Zernike square in unit of account circle
Zernike square is normalized using 0 rank geometric moment
5. finger vein identification method according to claim 1, it is characterised in that: during referring to hand vein recognition, by In posture difference, illumination difference that finger is put into.It is difficult to ensure that same individual is referring to the template and matching acquired when vein typing When the effective information that extracts be consistent.It is most of all to rotate, deviate.This brings no small choose to high-precision identification War.Based on this, the invention proposes a kind of improved Zernike square recognizers, carry out better characterize with this and refer to veinprint Global characteristics improve accuracy of identification.
CN201910417207.1A 2019-05-16 2019-05-16 A kind of finger vein identification method Pending CN110119724A (en)

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CN110852213A (en) * 2019-10-30 2020-02-28 天津大学 Template matching-based pointer instrument multi-condition automatic reading method
CN111639555A (en) * 2020-05-15 2020-09-08 圣点世纪科技股份有限公司 Finger vein image noise accurate extraction and self-adaptive filtering denoising method and device
CN111639557A (en) * 2020-05-15 2020-09-08 圣点世纪科技股份有限公司 Intelligent registration feedback method for finger vein image
CN111639560A (en) * 2020-05-15 2020-09-08 圣点世纪科技股份有限公司 Finger vein feature extraction method and device based on dynamic fusion of vein skeleton line and topographic relief characteristic
CN112287147A (en) * 2020-10-30 2021-01-29 华盛通(无锡)影像科技有限公司 Multi-template finger vein feature search algorithm based on bubbling sorting
CN113128378A (en) * 2021-04-06 2021-07-16 浙江精宏智能科技有限公司 Quick finger vein identification method

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Publication number Priority date Publication date Assignee Title
CN110852213A (en) * 2019-10-30 2020-02-28 天津大学 Template matching-based pointer instrument multi-condition automatic reading method
CN110852213B (en) * 2019-10-30 2023-12-12 天津大学 Pointer instrument multi-condition automatic reading method based on template matching
CN111639555A (en) * 2020-05-15 2020-09-08 圣点世纪科技股份有限公司 Finger vein image noise accurate extraction and self-adaptive filtering denoising method and device
CN111639557A (en) * 2020-05-15 2020-09-08 圣点世纪科技股份有限公司 Intelligent registration feedback method for finger vein image
CN111639560A (en) * 2020-05-15 2020-09-08 圣点世纪科技股份有限公司 Finger vein feature extraction method and device based on dynamic fusion of vein skeleton line and topographic relief characteristic
CN111639557B (en) * 2020-05-15 2023-06-20 圣点世纪科技股份有限公司 Intelligent registration feedback method for finger vein image
CN112287147A (en) * 2020-10-30 2021-01-29 华盛通(无锡)影像科技有限公司 Multi-template finger vein feature search algorithm based on bubbling sorting
CN113128378A (en) * 2021-04-06 2021-07-16 浙江精宏智能科技有限公司 Quick finger vein identification method
CN113128378B (en) * 2021-04-06 2022-07-19 浙江精宏智能科技有限公司 Finger vein rapid identification method

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