CN112861756A - Finger vein identification method and system - Google Patents
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- CN112861756A CN112861756A CN202110204692.1A CN202110204692A CN112861756A CN 112861756 A CN112861756 A CN 112861756A CN 202110204692 A CN202110204692 A CN 202110204692A CN 112861756 A CN112861756 A CN 112861756A
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- 238000000034 method Methods 0.000 title claims abstract description 27
- 108010054147 Hemoglobins Proteins 0.000 claims abstract description 101
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- 239000008280 blood Substances 0.000 claims description 8
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- 230000003190 augmentative effect Effects 0.000 claims description 6
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- 238000013441 quality evaluation Methods 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 230000002708 enhancing effect Effects 0.000 claims description 5
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- 108010036302 hemoglobin AS Proteins 0.000 claims description 3
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- 229960004134 propofol Drugs 0.000 claims description 3
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- 238000002601 radiography Methods 0.000 abstract description 9
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- 238000003384 imaging method Methods 0.000 description 2
- 235000002566 Capsicum Nutrition 0.000 description 1
- 239000006002 Pepper Substances 0.000 description 1
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- 235000008184 Piper nigrum Nutrition 0.000 description 1
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- 210000004204 blood vessel Anatomy 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 210000002615 epidermis Anatomy 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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Abstract
According to the finger vein identification method and system, the image sensor with the near-infrared sensing capability is used for capturing the image data of the hemoglobin radiography in the finger, the image acquisition capability of the hemoglobin radiography is higher than that of a common sensor, a coarse model is established according to the characteristics of hemoglobin for evaluating the image quality level, an image meeting the requirements can be screened out well, and the condition that the low-level image is attempted to be restored all the time is avoided. Through carrying out the preliminary treatment to the image that accords with the quality requirement, can remove the noise, highlight the hemoglobin radiography, then extract the hemoglobin radiography according to preset hemoglobin characteristic, compare according to with preset characteristic point set.
Description
Technical Field
The invention belongs to the technical field of biological feature recognition, and particularly relates to a finger vein recognition method and system.
Background
Along with the application and popularization of smart devices in modern society, authentication and verification methods are gradually changed from early card verification and password verification to human body biometric verification, such as fingerprint and iris verification. With the use of various biometric feature recognition techniques, related drawbacks are gradually emerging. As the most common fingerprint identification technology, fingerprints are easily worn and stolen, and the authentication device can be easily cheated by using one finger print. The security problem of biometric information has been a major obstacle to the spread of biometric identification devices.
The advent and emergence of finger vein recognition technology provides a new way for solving the technical problem of biometric information verification security. Finger vein recognition is fundamentally different from conventional fingerprint recognition, and has many advantages, such as: the finger vein recognition is realized according to a vein image formed by near infrared rays reflected by hemoglobin in a vein, belongs to a living body recognition technology, is not influenced by external environments such as epidermis, pollution, temperature and humidity due to the fact that the vein belongs to internal physiological characteristics, cannot be copied, and mainly depends on living body advantages brought by different structures in fingers due to different absorptivity of infrared light, advantages brought by non-contact verification and stability advantages that the recognition rate of the finger is not influenced by external damage of the finger, and the like. Since the concept of finger vein recognition was originally proposed by Joseph Rice, the technology has been used in the fields of civilian, finance and transportation after more than 20 years of exploration and development. However, due to the image sensor performance of different devices, device lighting conditions may cause the vein vessel image to be blurred or noise may mask the elongated vein vessel image to cause interruption. In the prior art, the ideal situation of finger vein identification comparison is based on the same or close imaging conditions, low-quality image comparison is less suitable, and the images of finger vein vessels in natural environments or other imaging devices have great differences, so that comparison with stored results in a database is difficult.
Disclosure of Invention
In order to solve the above problems, the present invention provides the following technical solutions:
a finger vein identification method comprises the following steps:
s1, capturing multiple frames of fingers containing the fingers to be collected by using an image sensor with near infrared sensing capabilityAnd (3) hemoglobin image data formed by hemoglobin absorption of near infrared light in flowing blood. Can adoptSenoricsThe high-precision CMOS image sensor.
S2: the quality evaluation is performed on the captured image data using the hemoglobin radiographing rough model, the quality grade is confirmed, and the image data of the hemoglobin radiographing characteristics that meet the quality requirements is input to S3.
S3, preprocessing the captured hemoglobin photographic image data of the finger vein to obtain data of the features to be extracted, wherein the preprocessing further comprises the following steps:
s31, enhancing image data;
s32, denoising the enhanced image data and highlighting a hemoglobin photographic image;
s33, carrying out target detection and extraction on the hemoglobin photographic image;
s34, collecting images containing a certain amount of hemoglobin by using an edge detection algorithm.
S4: extracting a second feature point image in the set containing the hemoglobin.
S5: and comparing the extracted feature points with a pre-stored first feature point image, comparing the second feature point set with the pre-stored first feature point set, judging whether the second feature point set is matched with the first feature point set within a threshold range, and if so, successfully identifying and comparing.
In step S31, the image data is copied to create a copy, the copy is image-inverted, and then image smoothing is performed after the image inversion, and then the image data is inverted and converted to obtain enhanced image data.
In step S32, the enhanced image data is processed by blocking, the image data is divided into a plurality of block images according to preset conditions, the block images are clustered, and then the solution is performed by using an augmented lagrange multiplier method, so as to obtain a de-noised highlighted hemoglobin photographic image.
In the step S33, a Region-normal (R-CNN) algorithm based on Region propofol is adopted to generate a hemoglobin target candidate box for the hemoglobin photographic image, and then the candidate box is classified and regressed, and most of hemoglobin is used as a target for detection and extraction.
A finger vein recognition system comprising one or more processors and memory, the memory storing a program and configured to perform the following steps by the one or more processors:
capturing a plurality of frames of image data for hemoglobin shadow formed by absorption of near infrared light by hemoglobin in flowing blood in a finger to be collected by using an image sensor with near infrared sensing capability;
performing quality evaluation on the captured image data by using a hemoglobin shadow rough model, confirming the quality grade, and inputting the image data of the hemoglobin shadow characteristics meeting the quality requirement into the next step;
preprocessing captured hemoglobin photographic image data of the finger veins to obtain data of features to be extracted, wherein the preprocessing further comprises the following steps:
enhancing the image data;
denoising the enhanced image data, and highlighting the hemoglobin photographic image;
carrying out target detection and extraction on the hemoglobin photographic image;
collecting an image containing a certain amount of hemoglobin by using an edge detection algorithm;
extracting a second feature point image in the set containing the hemoglobin;
and comparing the extracted feature points with a pre-stored first feature point image, comparing the second feature point set with the pre-stored first feature point set, judging whether the second feature point set is matched with the first feature point set within a threshold range, and if so, successfully identifying and comparing.
A finger vein recognition system, further comprising: copying the image data to create a copy, performing image inversion on the copy, performing image smoothing after the image inversion, and performing inversion conversion to obtain enhanced image data.
Further comprising: and partitioning the enhanced image data, dividing the image data into a plurality of block images according to preset conditions, processing, clustering the block images, and solving by adopting an augmented Lagrange multiplier method to obtain the noise-removed prominent hemoglobin photographic image.
Further comprising: and generating a hemoglobin target candidate frame for the hemoglobin photographic image by adopting an R-CNN algorithm based on Region Proposal, classifying and regressing the candidate frame, and detecting and extracting most of hemoglobin as a target.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the finger vein identification method and system, the image sensor with the near-infrared sensing capability is used for capturing the image data of the hemoglobin radiography in the finger, the image acquisition capability of the hemoglobin radiography is higher than that of a common sensor, a coarse model is established according to the characteristics of hemoglobin for evaluating the image quality level, an image meeting the requirements can be screened out well, and the condition that the low-level image is attempted to be restored all the time is avoided. Through carrying out the preliminary treatment to the image that accords with the quality requirement, can remove the noise, highlight the hemoglobin radiography, then extract according to the hemoglobin radiography of predetermined hemoglobin characteristic team, compare according to with predetermined characteristic point set.
2. The invention relates to a finger vein recognition method and a system, which are characterized in that image data are copied to create a copy, the copy is subjected to image inversion, image smoothing processing is carried out after the image inversion, then enhanced image data are obtained after the inversion conversion, as hemoglobin has the characteristics which are not obvious enough and is dissolved in blood, the background color of the blood which is irrelevant can be enhanced through the normal image enhancement, the contrast of the hemoglobin and other substances is pulled away as much as possible through the image inversion, the common bias of the technology is overcome, the effect of better contrast is brought, the accuracy of the hemoglobin radiography is restored as much as possible through the smoothing processing, and the image is enabled to be attached to the original shape.
3. According to the finger vein identification method and system, the enhanced image data are processed in a blocking mode, the image data are divided into the block images according to the preset conditions and processed, the overall processing speed can be improved, large operation resources do not need to be occupied, the hemoglobin can be highlighted and the edges are smooth through clustering operation and the Lagrange multiplier method, and the accuracy of subsequent comparison can be improved.
4. According to the finger vein identification method and system, the hemoglobin photo can be quickly selected from the image frame by adopting the R-CNN fine algorithm, the size of the pattern is reduced, and the proportion of the hemoglobin photo in the image is increased.
Drawings
FIG. 1 is a schematic flow chart of a finger vein recognition method according to the present invention;
FIG. 2 is a diagram illustrating a finger vein according to a finger vein recognition method of the present invention;
FIG. 3 is a diagram illustrating a finger vein after denoising according to a finger vein recognition method of the present invention;
fig. 4 is a schematic structural diagram of a finger vein recognition system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a finger vein recognition method includes the following steps:
s1, capturing a plurality of frames of image data for the finger to be collected, wherein the image data comprise hemoglobin shadow formed by absorption of near infrared light by hemoglobin in flowing blood by using an image sensor with near infrared sensing capability; as shown in fig. 2, fig. 2 is image data in an ideal case.
S2: and (3) performing quality evaluation on the captured image data by using a hemoglobin shadow rough model, confirming the quality grade, inputting the image data of the hemoglobin shadow characteristic meeting the quality requirement into S3, rejecting the image data which do not meet the quality requirement, and stopping if the image data do not meet the quality requirement, wherein the basis of the quality grade evaluation is hemoglobin, and whether the thin blood vessels can be partially restored by noise or not. And establishing a coarse model according to the characteristics of the hemoglobin, such as shape, chroma and the like, for evaluating the image quality grade, if the coarse models are not matched, the frame of picture cannot be subjected to subsequent identification, and the image meeting the requirements can be better screened out by setting the coarse model.
S3, preprocessing the captured hemoglobin photographic image data of the finger vein to obtain data of the features to be extracted, wherein the preprocessing further comprises the following steps:
and S21, enhancing the image data. The contrast of the overall image is enhanced.
And S22, denoising the enhanced image data, wherein salt and pepper noises and random noises are mainly removed, and the hemoglobin photographic image is highlighted.
And S23, carrying out target detection and extraction on the hemoglobin photographic image. Target detection is performed based on the characteristics of hemoglobin and a target that is considered likely to be a hemoglobin contrast image is extracted.
S24, collecting images containing a certain amount of hemoglobin by using an edge detection algorithm. The edge detection is to identify hemoglobin with obvious light and dark contrast in the picture. After a series of collection, extraction and reduction, the product is shown in figure 3.
S3: and extracting a second feature point image in the set containing the hemoglobin, as shown in FIG. 3.
S4: and comparing the extracted feature points with a pre-stored first feature point image, comparing the second feature point set with the pre-stored first feature point set, judging whether the second feature point set is matched with the first feature point set within a threshold range, and if so, successfully identifying and comparing.
In step S21, the hemoglobin photographic image data is copied to create a copy, the copy is subjected to image inversion, noise and hemoglobin are strongly contrasted, image smoothing is performed after inversion, and then inversion conversion is performed to obtain enhanced image data.
In step S22, the enhanced image data is processed by blocking, the image data is divided into a plurality of block images according to preset conditions, the block images are clustered, and then the solution is performed by using an augmented lagrange multiplier method, so as to obtain a de-noised highlighted hemoglobin photographic image.
In the step S23, a Region-normal (R-CNN) algorithm based on Region propofol is adopted to generate a hemoglobin target candidate box for the hemoglobin photographic image, and then the candidate box is classified and regressed, and most of hemoglobin is used as a target for detection and extraction.
A finger vein recognition system, as shown in fig. 4, comprising a finger viewing area 1, a finger placed at an acquisition port 2 to acquire an image of a finger vein, comprising one or more processors and a memory, said memory storing a program and configured to be executed by said one or more processors to perform the steps of:
the method comprises the steps of capturing multiple frames of image data for hemoglobin shadow formed by absorption of near infrared light by hemoglobin in flowing blood in a finger to be collected by using an image sensor with near infrared sensing capability.
And performing quality evaluation on the captured image data by using a hemoglobin shadow rough model, confirming the quality grade, and inputting the image data of the hemoglobin shadow characteristics meeting the quality requirement into the next step.
Preprocessing captured hemoglobin photographic image data of the finger veins to obtain data of features to be extracted, wherein the preprocessing further comprises the following steps:
the image data is enhanced.
Denoising the enhanced image data, and highlighting the hemoglobin photographic image.
And carrying out target detection and extraction on the hemoglobin photographic image.
An image containing a certain amount of hemoglobin will be collected using an edge detection algorithm.
Extracting a second feature point image in the set containing the hemoglobin.
And comparing the extracted feature points with a pre-stored first feature point image, comparing the second feature point set with the pre-stored first feature point set, judging whether the second feature point set is matched with the first feature point set within a threshold range, and if so, successfully identifying and comparing.
Further comprising: copying the image data to create a copy, performing image inversion on the copy, performing image smoothing after the image inversion, and performing inversion conversion to obtain enhanced image data.
Further comprising: and partitioning the enhanced image data, dividing the image data into a plurality of block images according to preset conditions, processing, clustering the block images, and solving by adopting an augmented Lagrange multiplier method to obtain the noise-removed prominent hemoglobin photographic image.
Further comprising: and generating a hemoglobin target candidate frame for the hemoglobin photographic image by adopting an R-CNN algorithm based on Region Proposal, classifying and regressing the candidate frame, and detecting and extracting most of hemoglobin as a target.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (8)
1. A finger vein recognition method is characterized by comprising the following steps:
s1, capturing a plurality of frames of image data for hemoglobin shadow formed by absorption of near infrared light by hemoglobin in flowing blood in the finger to be collected by using an image sensor with near infrared sensing capability;
s2: performing quality evaluation on the captured image data by using a hemoglobin shadow rough model, confirming the quality grade, and inputting the image data of the hemoglobin shadow characteristics meeting the quality requirement into S3;
s3, preprocessing the captured hemoglobin photographic image data of the finger vein to obtain data of the features to be extracted, wherein the preprocessing further comprises the following steps:
s31, enhancing image data;
s32, denoising the enhanced image data and highlighting a hemoglobin photographic image;
s33, carrying out target detection and extraction on the hemoglobin photographic image;
s34, collecting images containing a certain amount of hemoglobin by using an edge detection algorithm;
s4: extracting a second feature point image in the set containing the hemoglobin;
s5: and comparing the extracted feature points with a pre-stored first feature point image, comparing the second feature point set with the pre-stored first feature point set, judging whether the second feature point set is matched with the first feature point set within a threshold range, and if so, successfully identifying and comparing.
2. The method according to claim 1, wherein in step S31, the image data is copied to create a copy, the copy is image-inverted, and then image smoothing is performed after the image inversion, and then the image data is inverted and converted to obtain enhanced image data.
3. The finger vein recognition method according to claim 2, wherein: in step S32, the enhanced image data is processed by blocking, the image data is divided into a plurality of block images according to preset conditions, the block images are clustered, and then the solution is performed by using an augmented lagrange multiplier method, so as to obtain a de-noised highlighted hemoglobin photographic image.
4. A finger vein recognition method according to claim 3, characterized in that: in the step S33, a Region-normal (R-CNN) algorithm based on Region propofol is adopted to generate a hemoglobin target candidate box for the hemoglobin photographic image, and then the candidate box is classified and regressed, and most of hemoglobin is used as a target for detection and extraction.
5. A finger vein recognition system, characterized by: comprising one or more processors and memory, the memory storing a program and configured to perform the following steps by the one or more processors:
capturing a plurality of frames of image data of hemoglobin shadow formed by absorption of near infrared light by hemoglobin in flowing blood in a finger to be collected by using an image sensor with near infrared sensing capability;
performing quality evaluation on the captured image data by using a hemoglobin shadow rough model, confirming the quality grade, and inputting the image data of the hemoglobin shadow characteristics meeting the quality requirement into the next step;
preprocessing captured hemoglobin photographic image data of the finger veins to obtain data of features to be extracted, wherein the preprocessing further comprises the following steps:
enhancing the image data;
denoising the enhanced image data, and highlighting the hemoglobin photographic image;
carrying out target detection and extraction on the hemoglobin photographic image;
collecting an image containing a certain amount of hemoglobin by using an edge detection algorithm;
extracting a second feature point image in the set containing the hemoglobin;
and comparing the extracted feature points with a pre-stored first feature point image, comparing the second feature point set with the pre-stored first feature point set, judging whether the second feature point set is matched with the first feature point set within a threshold range, and if so, successfully identifying and comparing.
6. A finger vein identification system according to claim 5, further comprising: copying the image data to create a copy, performing image inversion on the copy, performing image smoothing after the image inversion, and performing inversion conversion to obtain enhanced image data.
7. The finger vein recognition system of claim 6, further comprising: and partitioning the enhanced image data, dividing the image data into a plurality of block images according to preset conditions, processing, clustering the block images, and solving by adopting an augmented Lagrange multiplier method to obtain the noise-removed prominent hemoglobin photographic image.
8. The finger vein recognition system of claim 6, further comprising: and generating a hemoglobin target candidate frame for the hemoglobin photographic image by adopting an R-CNN algorithm based on Region Proposal, classifying and regressing the candidate frame, and detecting and extracting most of hemoglobin as a target.
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Application publication date: 20210528 |