Comprehensive evaluation method and system for finger vein image quality

Abstract

The invention discloses a comprehensive evaluation method and system for finger vein image quality. The method comprises the following steps: acquiring a plurality of corrected finger vein images; carrying out filtering processing on the corrected finger vein image by adopting a nonlinear median filtering method; performing primary screening on the gray-scale finger vein image by using a gray-scale contrast evaluation model; a dynamic threshold segmentation method is adopted to carry out binarization processing on the gray-scale finger vein images qualified through preliminary screening; based on the binarized finger vein image and the connected domain marking characteristics thereof, acquiring a single-pixel binarized finger vein image by adopting a skeleton extraction method; calculating a texture distribution saturation parameter; the number of the finger vein burrs is obtained by judging the finger vein burrs; judging the pseudo vein textures to obtain the number of the pseudo vein textures; and constructing a finger vein image quality evaluation model based on the texture distribution saturation parameter, the number of finger vein burrs and the number of pseudo vein textures, and performing quality evaluation on the finger vein image by using the evaluation model.

Description

Technical Field

[0001] The invention relates to a comprehensive evaluation method for finger vein image quality and also to a corresponding comprehensive evaluation system for finger vein image quality, belonging to the technical field of biometric feature recognition. Background Art

[0002] With the continuous development of information technology, the requirements for the security protection of personal identity information are also increasing. Traditional identity authentication methods based on magnetic cards, certificates, passwords, etc. have problems such as easy loss, easy theft, and easy copying, and cannot guarantee the security of personal identity information. In recent years, biometric recognition technology, as a personal identity authentication method, uses the inherent characteristic attributes of a person as an identity authentication mark, and has been increasingly widely used due to its good adaptability and security. Unlike the first-generation biometric recognition technologies such as fingerprint recognition or face recognition, finger vein recognition technology extracts vein features from the collected finger vein near-infrared image, and then performs pattern matching with the feature library to achieve the purpose of identity recognition and authentication. Finger vein recognition technology has its own advantages such as liveness recognition and internal features, and is not easy to forge features. Compared with the first-generation biometric recognition technology, it has higher security, better stability and high efficiency of identity authentication.

[0003] In the prior art, most of the research on finger vein authentication systems is based on the authentication research of two-dimensional finger vein images. Due to the limited amount of information carried by two-dimensional finger vein images, it is difficult for the authentication system to maintain its original high accuracy performance advantage in application scenarios with a larger number of users. On the other hand, in the process of collecting finger vein images, due to the influence of changes in ambient light, differences in finger placement, and finger disturbances, image quality problems such as unclear image boundaries often occur, which in turn leads to high rejection rates and false recognition rates of the finger vein recognition system, and it is difficult for the authentication system to accurately identify and other problems. In addition, since additional near-infrared light sources and specific image sensors are required when acquiring finger vein images, when there are differences between the finger vein acquisition devices deployed at different terminals, the acquired vein images will be different, which will have an adverse effect on the authentication performance. Therefore, how to construct a comprehensive evaluation model for finger vein image quality that can quickly evaluate the quality of the finger vein acquisition images to be identified and screen out defective finger vein images is of great significance to improving the accuracy and precision of the existing finger vein recognition and authentication systems.

[0004] In the Chinese invention patent with patent number ZL 202010007188.8, a method for quantitative evaluation of finger vein image quality is disclosed. The method includes: calculating the grayscale distribution index of the finger vein image; calculating the noise level index of the finger vein image; calculating the first-order gradient index of the finger vein image; calculating the second-order gradient index of the finger vein image; constructing a mathematical model for quantitative evaluation of finger vein image quality and calculating model parameters; using the constructed mathematical model for quantitative evaluation of finger vein image quality to quantitatively evaluate image quality under different environments. Summary of the invention

[0005] The primary technical problem to be solved by the present invention is to provide a comprehensive evaluation method for finger vein image quality.

[0006] Another technical problem to be solved by the present invention is to provide a comprehensive evaluation system for finger vein image quality.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] According to a first aspect of an embodiment of the present invention, a comprehensive evaluation method for finger vein image quality is provided, comprising the following steps:

[0009] (1) acquiring a plurality of calibrated finger vein images from a finger vein recognition system;

[0010] (2) filtering the corrected finger vein image using a nonlinear median filtering method to obtain a grayscale finger vein image;

[0011] (3) performing a preliminary screening on the grayscale finger vein image using a grayscale contrast evaluation model to obtain the grayscale finger vein image that passes the preliminary screening;

[0012] (4) using a dynamic threshold segmentation method to perform binarization processing on the grayscale finger vein image that has passed the initial screening to obtain a binary finger vein image;

[0013] (5) Based on the binary finger vein image and its connected domain marking characteristics, a skeleton extraction method is used to obtain a single-pixel binary finger vein image;

[0014] (6) calculating a texture distribution saturation parameter of the single-pixel binary finger vein image;

[0015] (7) obtaining the number of finger vein burrs in the single-pixel binary finger vein image by determining the finger vein burrs;

[0016] (8) Obtaining the number of pseudo-vein textures of the single-pixel binary finger vein image by determining the pseudo-vein texture;

[0017] (9) A finger vein image quality assessment model is constructed based on the texture distribution saturation parameter, the number of finger vein burrs, and the number of pseudo-vein textures, and the quality of the finger vein image is assessed using the assessment model.

[0018] Preferably, step (1) includes the following sub-steps:

[0019] (11) obtaining an original finger vein video stream in a time dimension from the finger vein recognition system;

[0020] (12) Using a plane chessboard calibration method to calibrate the infrared camera parameters in the finger vein recognition system to obtain the camera's intrinsic parameters and distortion coefficients;

[0021] (13) Using the intrinsic parameters and the distortion coefficients, an image distortion correction method is used to correct the original finger vein video stream to obtain a corrected finger vein video stream and the multiple finger vein images contained therein.

[0022] Preferably, in step (2), the grayscale finger vein image g(x, y) satisfies the following formula:

[0023] g(x,y)=med{f(xm,yn)};m,n∈[-w1,+w1]

[0024] Among them, med means taking the median; f(x, y) represents the finger vein image to be filtered; w1 is a positive integer used to represent the half-width size of the sliding window; m and n are integers, and (xm, yn) represents the coordinates of the pixel point in the sliding window.

[0025] Preferably, step (3) includes the following sub-steps:

[0026] (31) obtaining an average grayscale value by calculating a grayscale histogram of the grayscale finger vein image;

[0027] (32) Based on the difference of grayscale values ​​at different pixel coordinate positions, a mean square error algorithm is used to construct the grayscale contrast evaluation model of the finger, and the grayscale contrast of the grayscale finger vein image is calculated;

[0028] (33) comparing the grayscale contrast with a grayscale contrast threshold to preliminarily screen the quality of the grayscale finger vein image;

[0029] When the grayscale contrast is greater than or equal to the grayscale contrast threshold, the quality of the current grayscale finger vein image meets the initial screening requirement; otherwise, the current grayscale finger vein image fails the initial screening.

[0030] Preferably, in step (32), the grayscale contrast C rThe calculation satisfies the following formula:

[0031]

[0032] Among them, v i is the gray value of the i-th pixel in the image; v m is the average gray value; N total is the total number of pixels in the image.

[0033] Preferably, in step (6), the calculation of the texture distribution saturation parameter α satisfies the following formula:

[0034]

[0035] Among them, k M , k N are all positive integers, representing the equal fractions along the horizontal and vertical directions of the resolution of the single-pixel binary finger vein image; T r k M *k N The total number of block areas with finger vein texture information in the block areas.

[0036] Preferably, step (7) includes the following sub-steps:

[0037] (71) Determine the bifurcation point of the finger vein texture;

[0038] (72) determining the endpoints of the finger vein texture, and calculating the pixel length from the bifurcation point to the adjacent endpoints;

[0039] (73) Determine the finger vein burrs in the finger vein texture and calculate the number of the finger vein burrs.

[0040] Preferably, in step (9),

[0041] The output value E of the finger vein image quality assessment model satisfies the following formula:

[0042] E=f(α,β,γ)

[0043] Among them, E is the output value of the model, which is a parameter representing the quality of the finger vein image; α is the texture distribution saturation parameter; β is the number of finger vein burrs; and γ is the number of pseudo-venous textures.

[0044] According to a second aspect of an embodiment of the present invention, a comprehensive evaluation system for finger vein image quality is provided, the system comprising a processor and a memory, the processor and the memory being coupled; wherein:

[0045] The memory is used to store computer programs;

[0046] The processor is used to run the computer program stored in the memory to execute the comprehensive evaluation method of finger vein image quality as described above.

[0047] Compared with the prior art, the comprehensive evaluation method of finger vein image quality provided by the present invention uses a grayscale contrast evaluation model to preliminarily screen finger vein images with low imaging quality, and constructs a finger vein image quality evaluation model based on texture distribution saturation parameters, the number of vein burrs and the number of pseudo-vein textures from the perspective of vein texture structure characteristics and their information entropy. The technical solution of secondary comprehensive evaluation and screening of finger vein images ensures the quality of finger vein images subsequently entering the finger vein recognition system for identification, and reduces the misidentification phenomenon caused by poor imaging quality due to ambient lighting, finger position disturbance, etc. Therefore, the comprehensive evaluation method of finger vein image quality provided by the present invention has the beneficial effects of strong environmental adaptability, high anti-interference ability, high recognition accuracy and good stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flowchart of a comprehensive evaluation method for finger vein image quality provided by the present invention;

[0049] Figure 2 is a schematic diagram of a binary finger vein image in an embodiment of the present invention;

[0050] Figure 3 is a schematic diagram of a finger vein texture being a finger vein burr in an embodiment of the present invention;

[0051] Figure 4 is a schematic diagram of a finger vein texture being a pseudo vein in an embodiment of the present invention;

[0052] Figure 5 A schematic diagram of the structure of a comprehensive evaluation system for finger vein image quality provided by the present invention. DETAILED DESCRIPTION

[0053] The technical content of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] like Figure 1 As shown, a comprehensive evaluation method for finger vein image quality provided by an embodiment of the present invention comprises at least the following steps:

[0055] S1: Acquire multiple rectified finger vein images from a finger vein recognition system.

[0056] When the finger vein recognition system identifies a person's finger, a monocular infrared camera generates an original finger vein video stream in the time dimension, which includes multiple frames of original finger vein images with timestamps. The acquisition of multiple corrected finger vein images includes the following sub-steps:

[0057] S11: Obtaining an original finger vein video stream in a time dimension from a finger vein recognition system.

[0058] S12: Use the plane chessboard calibration method to calibrate the infrared camera parameters in the finger vein recognition system to obtain the camera's intrinsic parameters K in And distortion coefficient Dist.

[0059] S13: Using the internal parameter K in and distortion coefficient Dist, and use the image distortion correction method to correct the original finger vein video stream to obtain the corrected finger vein video stream and multiple finger vein images f(x, y) contained therein; where (x, y) is the coordinate value of the image pixel.

[0060] S2: Using a nonlinear median filtering method to filter the corrected finger vein image to obtain a grayscale finger vein image.

[0061] The purpose of filtering the corrected finger vein image is to reduce the interference noise generated during the acquisition process of the finger vein image while ensuring the texture structure information of the finger vein. The specific method of filtering is to select a sliding window of a fixed size [2w1+1,2w1+1] with the target pixel position on each finger vein image as the center, where w1 is a positive integer used to represent the half-width size of the sliding window. The pixel grayscale values ​​in the sliding window of the image are sorted, and the middle value is taken as the new grayscale value of the target pixel, thereby forming a grayscale finger vein image.

[0062] Assuming that the finger vein image to be filtered is f(x, y), and the grayscale finger vein image after filtering is g(x, y), the expression of the grayscale finger vein image g(x, y) is:

[0063] g(x,y)=med{f(xm,yn)};m,n∈[-w1,+w1] (1)

[0064] Among them, med means taking the median; m and n are integers, and (xm,yn) represents the coordinates of the pixel point in the sliding window.

[0065] For example, when w1=1, the sliding window [2w1+1,2w1+1] is represented as a 3×3 pixel area, with the target pixel position (x0, y0) as the center, and the grayscale values ​​of 9 pixel positions including the center point can be obtained as shown in Table 1.

[0066] Table 1

[0067] <![CDATA[f(x0-1,y0+1)]]> <![CDATA[f(x0,y0+1)]]> <![CDATA[f(x0+1,y0+1)]]> <![CDATA[f(x0-1,y0)]]> <![CDATA[f(x0,y0)]]> <![CDATA[f(x0+1,y0)]]> <![CDATA[f(x0-1,y0-1)]]> <![CDATA[f(x0,y0-1)]]> <![CDATA[f(x0+1,y0-1)]]>

[0068] Formula (1) indicates that by sorting the grayscale values ​​of the above 9 pixels from large to small, the median of the grayscale values ​​is selected as the new grayscale value of the target pixel position (x0, y0), thereby obtaining the grayscale finger vein image g(x, y).

[0069] S3: Perform a preliminary screening on the grayscale finger vein image using the grayscale contrast evaluation model to obtain a grayscale finger vein image that passes the preliminary screening.

[0070] Since the clarity of the vein texture structure directly determines the quality of the finger vein image, in order to prevent the appearance of blurred and difficult-to-identify finger vein images during the recognition process, it is necessary to build a grayscale contrast evaluation model before performing finger vein image quality evaluation to pre-eliminate low-quality finger vein images. The specific method of the initial screening includes the following sub-steps:

[0071] S31: By calculating the grayscale histogram of the grayscale finger vein image, the average grayscale value v is obtained m ;

[0072] S32: Based on the difference of grayscale values ​​at different pixel coordinate positions, a mean square error algorithm is used to construct a finger grayscale contrast evaluation model, and the grayscale contrast of the grayscale finger vein image is calculated using the grayscale contrast evaluation model. Grayscale contrast C r The calculation is as follows:

[0073]

[0074] Among them, v i is the gray value of the i-th pixel in the image; N total is the total number of pixels in the image.

[0075] S33: The grayscale contrast C of the grayscale finger vein image is r Compared with the grayscale contrast threshold τ, the quality of the grayscale finger vein image is preliminarily screened. r When it is greater than or equal to the grayscale contrast threshold τ, it is considered that the quality of the current grayscale finger vein image meets the initial screening requirements, otherwise it is considered that the grayscale finger vein image fails the initial screening and is marked as a defective finger vein image.

[0076] S4: A dynamic threshold segmentation method is used to binarize the grayscale finger vein image that has passed the initial screening to obtain a binary finger vein image.

[0077] Binarization of the grayscale finger vein images that have passed the initial screening can characterize the quality of the finger vein images from the binary dimension. The specific implementation method is to select the corresponding neighborhood window [2w2+1,2w2+1] for each pixel in the image, where w2 is a positive integer, which is used to represent the half-width size of the neighborhood window. By calculating the mean m(x,y) and variance σ(x,y) of the pixels in the neighborhood window, the binarization segmentation threshold T(x,y) within the neighborhood window is determined.

[0078] The calculation of the binary segmentation threshold T(x,y) is as follows:

[0079] T(x,y)=m(x,y)+kσ(x,y) (3)

[0080] Wherein, k is a preset correction coefficient; (x, y) is the coordinate value of the image pixel.

[0081] By traversing different regions of the image through the above method, the binary processing of the overall grayscale finger vein image is completed, such as Figure 2 As shown, a binary finger vein image with coarser vein texture can be obtained.

[0082] S5: Based on the binary finger vein image and its connected domain marking characteristics, a skeleton extraction method is used to obtain a single-pixel binary finger vein image.

[0083] In order to achieve accurate characterization of a more stable topological structure of finger vein features, the present invention adopts a skeleton extraction method based on the binary finger vein image and its binary connected domain marking characteristics to further obtain a finger vein texture map with a single pixel width, that is, a single pixel binary finger vein image to be evaluated.

[0084] In the quality evaluation process of single-pixel binary finger vein images, considering the imaging and subsequent algorithm matching characteristics of finger veins, the present invention intends to construct an image quality evaluation model based on texture distribution saturation, vein burr noise and pseudo-vein texture from the perspective of finger vein texture distribution characteristics and its corresponding information entropy, and perform a secondary evaluation of the vein image quality.

[0085] S6: Calculate the texture distribution saturation parameter of the single-pixel binary finger vein image.

[0086] In terms of the saturation of the finger vein texture distribution, the texture information entropy is characterized based on the uniformity of the texture distribution of the finger vein image. The specific calculation method of the texture distribution saturation parameter is as follows.

[0087] Assume that the resolution of the single-pixel binary finger vein image to be evaluated is [M, N], where M and N are both positive integers, representing the horizontal and vertical resolutions respectively; k M and k NEqually divided (k M and k N is a positive integer), the whole image can be divided into k m *k n block regions, by judging whether each block region has finger vein texture information, the total number of block regions with finger vein texture information T is obtained by statistics. r , and finally the texture distribution saturation parameter α is obtained, which is calculated as follows:

[0088]

[0089] S7: by determining the finger vein burrs, obtaining the number of finger vein burrs in the single-pixel binary finger vein image.

[0090] In terms of finger vein burr noise, since the edge of the binary vein image often has tiny vein burrs, it will have an adverse effect on the texture structure matching and expression of the actual finger vein. Therefore, the present invention introduces the number of finger vein burrs as a parameter for its quality evaluation. The specific implementation method includes the following sub-steps:

[0091] S71: Determine the bifurcation point of the finger vein texture;

[0092] Taking the finger vein texture end at the upper left corner of the single-pixel binary finger vein image as the starting point, the pixel value of each pixel in the 8 adjacent neighborhoods is traversed in turn based on the connected domain marking characteristics. If only one pixel in the 8 neighborhoods has a non-zero pixel value, the tracking continues along the non-zero point until the number of non-zero points in the 8 neighborhoods of the non-zero point P is greater than 1, then the non-zero point P is considered to be the bifurcation point of the finger vein texture.

[0093] S72: Determine the endpoints of the finger vein texture, and calculate the pixel length from the bifurcation point to the adjacent endpoint.

[0094] Take the bifurcation point P as the starting point to re-track, and continue tracking according to the traversal method of the connected domain marking characteristics mentioned above until there are no new texture pixels. It is considered that the tracking reaches the endpoint of the finger vein texture, and the finger vein burr is judged by calculating the pixel length from the bifurcation point to the adjacent endpoint.

[0095] S73: determining the finger vein burrs in the finger vein texture, and calculating the number of the finger vein burrs;

[0096] The method for judging the finger vein burr is to obtain the pixel length threshold L from the bifurcation point to the adjacent endpoint in the standard finger vein texture through statistical analysis. th In the finger vein texture of the current image, if the pixel length value from the bifurcation point to the adjacent endpoint is less than the corresponding pixel length threshold L th , then the finger vein texture is judged to be a finger vein burr, such as Figure 3 As shown in the figure, the number of finger vein burrs β in the overall image can be obtained by judging and counting them one by one.

[0097] S8: Obtaining the number of pseudo vein textures of the single-pixel binary finger vein image by judging the pseudo vein texture.

[0098] In terms of pseudo-vein texture, based on the spatial continuity assumption of finger vein images, the isolated pseudo-vein textures are judged and counted respectively.

[0099] The specific implementation method is similar to the above-mentioned method for judging finger vein burrs. The pixel points with a pixel value of 1 in each texture feature connected area in the single-pixel binary finger vein image are counted to obtain the pixel length of the isolated finger vein texture. Then, the pixel length of the isolated finger vein texture is compared with the pixel length threshold L of the isolated finger vein texture. f The pseudo-vein texture is determined by comparing the pixel length threshold L of the isolated finger vein texture. f It is preset based on the resolution of acquiring the finger vein image.

[0100] If the pixel length of the isolated finger vein texture is less than the corresponding pixel length threshold L f , then the finger vein texture is judged to be a pseudo-vein texture, such as Figure 4 As shown in Figure 2, the number of pseudo vein textures γ on the overall finger vein image can be obtained by traversing all pixel points of the image in sequence.

[0101] S9: A finger vein image quality assessment model is constructed based on the texture distribution saturation parameter α, the number of finger vein burrs β and the number of pseudo vein textures γ, and the quality of the vein image is assessed using the assessment model.

[0102] The expression of the output value E of the evaluation model is:

[0103] E=f(α,β,γ) (5)

[0104] Among them, E is the output value of the evaluation model, which refers to the characterization parameter of the vein image quality.

[0105] The evaluation model E can calculate the corresponding characterization parameter E of the finger vein image quality based on the saturation parameter of the finger vein texture distribution, the number of finger vein burrs and the number of pseudo-vein textures. The quality of the finger vein image is evaluated and judged by comparing the characterization parameter E output by the evaluation model with the characterization parameter threshold E0. The characterization parameter threshold E0 is a pre-set characterization parameter threshold of the finger vein image quality based on the statistical analysis of the quality of typical finger vein images.

[0106] When the characterization parameter E output by the evaluation model is greater than or equal to the characterization parameter threshold E0, it is judged that the quality of the current finger vein image meets the requirements of subsequent recognition processing; otherwise, it is judged that the quality of the current finger vein image is low and is filtered out. The comprehensive quality evaluation of multiple vein images by the evaluation model can improve the accuracy and stability of the subsequent finger vein recognition system in finger vein image recognition.

[0107] The above is a detailed description of a comprehensive evaluation method for finger vein image quality provided by the present invention. Based on the comprehensive evaluation method for finger vein image quality, an embodiment of the present invention further provides a comprehensive evaluation system for finger vein image quality, such as Figure 5 As shown, the comprehensive evaluation system includes one or more processors and a memory. The memory is coupled to the processor and is used to store one or more computer programs. When the one or more computer programs are executed by the one or more processors, the one or more processors implement the comprehensive evaluation method for the quality of the finger vein image in the above embodiment.

[0108] The processor is used to control the overall operation of the comprehensive evaluation system for the finger vein image quality to complete all or part of the steps of the comprehensive evaluation method for the finger vein image quality. The processor module can be a central processing unit (CPU), a graphics processing unit (GPU), a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a digital signal processing (DSP) chip, etc. The memory is used to store various types of data to support operations on the comprehensive evaluation system for the finger vein image quality, and these data can include, for example, instructions for any application or method for the operation of the comprehensive evaluation system for the finger vein image quality, and data related to the application. The memory module can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, etc.

[0109] The above-mentioned comprehensive evaluation system for finger vein image quality can improve the accuracy and stability of finger vein image recognition by subsequent finger vein recognition systems by performing primary screening and secondary evaluation on multiple finger vein images provided by the finger vein recognition system, removing finger vein images with poor imaging quality and retaining finger vein images that are judged to be qualified by comprehensive evaluation.

[0110] Compared with the prior art, the comprehensive evaluation method of finger vein image quality provided by the present invention uses a grayscale contrast evaluation model to preliminarily screen finger vein images with low imaging quality, and constructs a finger vein image quality evaluation model based on texture distribution saturation parameters, the number of vein burrs and the number of pseudo-vein textures from the perspective of vein texture structure characteristics and their information entropy. The technical solution of secondary comprehensive evaluation and screening of finger vein images ensures the quality of finger vein images subsequently entering the finger vein recognition system for identification, and reduces the misidentification phenomenon caused by poor imaging quality due to ambient lighting, finger position disturbance, etc. Therefore, the comprehensive evaluation method of finger vein image quality provided by the present invention has the beneficial effects of strong environmental adaptability, high anti-interference ability, high recognition accuracy and good stability.

[0111] It should be noted that, in the description of the present invention, “plurality” means two or more than two, unless otherwise clearly and specifically defined.

[0112] The above is a detailed description of the comprehensive evaluation method and system for the finger vein image quality provided by the present invention. For those skilled in the art, any obvious changes made to it without departing from the essence of the present invention will constitute an infringement of the patent right of the present invention and will bear corresponding legal responsibilities.

Claims

1. A comprehensive evaluation method for finger vein image quality, characterized in that The steps include: (1) acquiring a plurality of calibrated finger vein images from a finger vein recognition system; (2) filtering the corrected finger vein image using a nonlinear median filtering method to obtain a grayscale finger vein image; (3) performing a preliminary screening on the grayscale finger vein image using a grayscale contrast evaluation model to obtain the grayscale finger vein image that passes the preliminary screening; (4) using a dynamic threshold segmentation method to perform binarization processing on the grayscale finger vein image that has passed the initial screening to obtain a binary finger vein image; (5) Based on the binary finger vein image and its connected domain marking characteristics, a skeleton extraction method is used to obtain a single-pixel binary finger vein image; (6) calculating a texture distribution saturation parameter of the single-pixel binary finger vein image; (7) obtaining the number of finger vein burrs in the single-pixel binary finger vein image by determining the finger vein burrs; (8) Obtaining the number of pseudo-vein textures of the single-pixel binary finger vein image by determining the pseudo-vein texture; (9) A finger vein image quality assessment model is constructed based on the texture distribution saturation parameter, the number of finger vein burrs, and the number of pseudo-vein textures, and the quality of the finger vein image is assessed using the assessment model.

2. The comprehensive evaluation method for finger vein image quality according to claim 1, characterized in that Step (1) includes the following sub-steps: (11) obtaining an original finger vein video stream in a time dimension from the finger vein recognition system; (12) Using a plane chessboard calibration method to calibrate the infrared camera parameters in the finger vein recognition system to obtain the camera's intrinsic parameters and distortion coefficients; (13) Using the intrinsic parameters and the distortion coefficients, an image distortion correction method is used to correct the original finger vein video stream to obtain a corrected finger vein video stream and the multiple finger vein images contained therein.

3. The comprehensive evaluation method for finger vein image quality according to claim 1, characterized in that In step (2), the grayscale finger vein image g(x, y) satisfies the following formula: g(x,y)=med{f(xm,yn)};m,n∈[-w1,+w1] Among them, med means taking the median; f(x, y) represents the finger vein image to be filtered; w1 is a positive integer used to represent the half-width size of the sliding window; m and n are integers, and (xm, yn) represents the coordinates of the pixel point in the sliding window.

4. The comprehensive evaluation method for finger vein image quality according to claim 1, characterized in that Step (3) includes the following sub-steps: (31) obtaining an average grayscale value by calculating a grayscale histogram of the grayscale finger vein image; (32) Based on the difference of grayscale values ​​at different pixel coordinate positions, a mean square error algorithm is used to construct the grayscale contrast evaluation model of the finger, and the grayscale contrast of the grayscale finger vein image is calculated; (33) comparing the grayscale contrast with a grayscale contrast threshold to preliminarily screen the quality of the grayscale finger vein image; When the grayscale contrast is greater than or equal to the grayscale contrast threshold, the quality of the current grayscale finger vein image meets the initial screening requirement; otherwise, the current grayscale finger vein image fails the initial screening.

5. The comprehensive evaluation method for finger vein image quality as claimed in claim 4, characterized in that In step (32), the grayscale contrast C r The calculation satisfies the following formula: Among them, v i is the gray value of the i-th pixel in the image; v m is the average gray value; N total is the total number of pixels in the image.

6. The comprehensive evaluation method for finger vein image quality according to claim 1, characterized in that In step (6), the calculation of the texture distribution saturation parameter α satisfies the following formula: Among them, k M , k N are all positive integers, representing the equal fractions along the horizontal and vertical directions of the resolution of the single-pixel binary finger vein image; T r k M *k N The total number of block areas with finger vein texture information in the block areas.

7. The comprehensive evaluation method for finger vein image quality according to claim 1, characterized in that Step (7) includes the following sub-steps: (71) Determine the bifurcation point of the finger vein texture; (72) determining the endpoints of the finger vein texture, and calculating the pixel length from the bifurcation point to the adjacent endpoints; (73) Determine the finger vein burrs in the finger vein texture and calculate the number of the finger vein burrs.

8. The comprehensive evaluation method for finger vein image quality according to claim 1, characterized in that In step (9), the output value E of the finger vein image quality assessment model satisfies the following formula: E=f(α,β,γ) Among them, E is the output value of the model, which is a parameter representing the quality of the finger vein image; α is the texture distribution saturation parameter; β is the number of finger vein burrs; and γ is the number of pseudo-venous textures.

9. A comprehensive evaluation system for finger vein image quality, characterized in that It includes a processor and a memory, the processor and the memory are coupled; wherein the memory is used to store a computer program; the processor is used to run the computer program stored in the memory to execute the comprehensive evaluation method for finger vein image quality as described in any one of claims 1 to 8.

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