Multi-level network identity authentication and access control system

Abstract

The application discloses a multi-level network identity authentication and access control system, which further introduces biometric recognition technology after traditional password verification, uses deep learning-based image processing technology to extract multi-scale features from user-input fingerprint images, captures shallow detail features and global structure features of the fingerprint, and obtains comprehensive feature representation of the fingerprint image through feature selection enhancement and multi-level feature joint perception processing, to generate a defogging fingerprint image, and then generate a final verification result based on pixel-by-pixel fingerprint difference information between the defogging fingerprint image and an authorized fingerprint image, so that only verified users can access systems or data corresponding to their roles. In this way, the problem of poor quality of fingerprint images can be effectively addressed, thereby improving the accuracy and security of identity authentication and optimizing the overall performance and user experience of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent control, and more specifically, to a multi-level network identity authentication and access control system. BACKGROUND

[0002] With the rapid development of information technology, network security problems have become increasingly prominent. In a network environment, ensuring the authenticity and legality of user identity is the foundation of system security. Traditional single password verification methods have been unable to meet the needs of modern information security, as passwords are easily forgotten, leaked, or cracked. In order to improve the security of identity authentication, multi-factor authentication has gradually become the mainstream trend, among which the multi-level identity authentication system combining password verification and biometric recognition technology is favored for its higher security and reliability.

[0003] Biometric recognition technologies, such as fingerprint recognition, facial recognition, iris recognition, etc., play an important role in identity authentication due to their uniqueness and difficulty in being forged. Among them, fingerprint recognition is one of the most mature and widely used biometric recognition technologies. Traditional fingerprint recognition systems usually rely on fingerprint sensors to directly acquire fingerprint images, which are then compared with fingerprint templates in the database through matching algorithms to verify user identity. However, existing verification systems often face problems such as low-quality fingerprint images and sensitivity to environmental changes in actual application. For example, in the case of a wet or stained finger, the quality of the fingerprint image may be affected, leading to a decrease in recognition accuracy.

[0004] Therefore, an optimized multi-level network identity authentication and access control system is expected. SUMMARY

[0005] To solve the above technical problems, the present application is proposed. The embodiments of the present application provide a multi-level network identity authentication and access control system, which further introduces biometric recognition technology after traditional password verification, uses deep learning-based image processing technology to extract multi-scale features from user-input fingerprint images, captures shallow detail features and global structural features of fingerprints, and through feature selection enhancement and multi-level feature joint perception processing, obtains comprehensive feature representation of fingerprint images to generate a defogging fingerprint image, and then generates the final verification result based on the pixel-by-pixel fingerprint difference information between the defogging fingerprint image and the authorized fingerprint image, ensuring that only verified users can access systems or data corresponding to their roles. In this way, the problem of poor quality of fingerprint images can be effectively addressed, thereby improving the accuracy and security of identity authentication and optimizing the overall performance and user experience of the system.

[0006] According to one aspect of the present application, a multi-level network identity authentication and access control system is provided, comprising:

[0007] User account information acquisition module, used to obtain the username and password entered by the user;

[0008] A user account verification module, configured to perform a first level of verification based on the user name and password;

[0009] A user fingerprint receiving module is used to receive a fingerprint image input by the user after the first layer verification is passed;

[0010] A user fingerprint verification module, configured to perform a second level of verification based on the fingerprint image;

[0011] an access request receiving module, configured to receive an access request input by the user after the second layer verification is passed;

[0012] An access rights verification module, configured to verify the user's access rights according to the RBAC policy;

[0013] The user fingerprint verification module includes:

[0014] A fingerprint image multi-scale feature extraction unit, configured to perform multi-scale feature extraction on the fingerprint image to obtain a fingerprint shallow feature map and a fingerprint structure feature map;

[0015] A feature selection and enhancement unit, configured to perform feature selection and enhancement processing on the fingerprint shallow feature map and the fingerprint structure feature map respectively to obtain a fingerprint shallow layer enhanced feature map and a fingerprint structure enhanced feature map;

[0016] a defogging fingerprint image generating unit, configured to generate a defogging fingerprint image based on the combined features of the fingerprint shallow layer enhancement feature map and the fingerprint structure enhancement feature map;

[0017] The verification result generating unit is used to determine whether the fingerprint authentication is passed based on the pixel difference between the defogging fingerprint image and the authorized fingerprint image.

[0018] Compared with the existing technology, the multi-level network identity authentication and access control system provided by this application further introduces biometric recognition technology after traditional password verification, and uses deep learning-based image processing technology to perform multi-scale feature extraction on the fingerprint image input by the user, capturing the shallow detail features and global structural features of the fingerprint, and through feature selection enhancement and multi-level feature joint perception processing, obtains a comprehensive feature representation of the fingerprint image, thereby generating a defogging fingerprint image, and then generates the final verification result based on the pixel-by-pixel fingerprint difference information between the defogging fingerprint image and the authorized fingerprint image, ensuring that only verified users can access the system or data that matches their role. In this way, the problem of poor fingerprint image quality can be effectively addressed, thereby improving the accuracy and security of identity authentication and optimizing the overall performance and user experience of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 is a block diagram of a multi-level network identity authentication and access control system according to an embodiment of the present application;

[0021] Figure 2 A data flow diagram of a multi-level network identity authentication and access control system according to an embodiment of the present application;

[0022] Figure 3 4 is a block diagram of a user fingerprint verification module in a multi-level network identity authentication and access control system according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0024] As used in this application and the claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not intended to refer to the singular but may include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0025] Although the present application makes various references to certain modules in the system according to embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are illustrative only, and different aspects of the system and method can use different modules.

[0026] Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the various steps may be processed in reverse order or simultaneously, as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0027] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0028] Traditional single-password authentication methods no longer meet modern information security requirements, as passwords are easily forgotten, leaked, or cracked. To improve identity security, multi-factor authentication is becoming a mainstream trend. Multi-layered authentication systems that combine password verification with biometrics are gaining popularity due to their enhanced security and reliability. Biometric technologies, such as fingerprint, facial, and iris recognition, play a crucial role in identity verification due to their uniqueness and resistance to forgery. Fingerprint recognition is one of the most mature and widely used biometric technologies. Traditional fingerprint recognition systems typically rely on a fingerprint sensor to directly capture a fingerprint image, which is then compared with a database of fingerprint templates using a matching algorithm to verify the user's identity. However, existing authentication systems often face challenges in practical applications, such as low fingerprint image quality and sensitivity to environmental changes. For example, if a finger is wet or stained, the fingerprint image quality may be affected, resulting in reduced recognition accuracy. Therefore, an optimized multi-layered network authentication and access control system is highly sought after.

[0029] In the technical solution of this application, a multi-level network identity authentication and access control system is proposed. Figure 1 4 is a block diagram of a multi-level network identity authentication and access control system according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of a multi-level network identity authentication and access control system according to an embodiment of the present application. Figure 1 and Figure 2As shown, the multi-level network identity authentication and access control system 300 according to an embodiment of the present application includes: a user account information acquisition module 310, used to obtain a user name and password input by a user; a user account verification module 320, used to perform a first-level verification based on the user name and the password; a user fingerprint receiving module 330, used to receive a fingerprint image input by the user after the first-level verification is passed; a user fingerprint verification module 340, used to perform a second-level verification based on the fingerprint image; an access request receiving module 350, used to receive an access request input by the user after the second-level verification is passed; and an access permission verification module 360, used to verify the user's access permission according to the RBAC policy.

[0030] Specifically, the user account information acquisition module 310 and the user account verification module 320 are configured to obtain a username and password entered by a user and perform a first level of verification based on the username and password. By verifying the username and password, the system can ensure that only authorized users can access protected resources or systems.

[0031] Specifically, the user fingerprint receiving module 330 is configured to receive a fingerprint image input by the user after the first level of verification has been passed. It should be understood that fingerprints are highly unique biometric features that can help organizations ensure that only authorized users can access protected resources or systems. Receiving a fingerprint image input by the user after the first level of verification (typically involving a username and password) has been passed can further enhance security.

[0032] In particular, the user fingerprint verification module 340 is used to perform a second level of verification based on the fingerprint image. In particular, in a specific example of the present application, as Figure 3 As shown, the user fingerprint verification module 340 includes: a fingerprint image multi-scale feature extraction unit 341, which is used to perform multi-scale feature extraction on the fingerprint image to obtain a fingerprint shallow feature map and a fingerprint structure feature map; a feature selection and enhancement unit 342, which is used to perform feature selection and enhancement processing on the fingerprint shallow feature map and the fingerprint structure feature map respectively to obtain a fingerprint shallow enhancement feature map and a fingerprint structure enhancement feature map; a defogging fingerprint image generation unit 343, which is used to generate a defogging fingerprint image based on the joint features of the fingerprint shallow enhancement feature map and the fingerprint structure enhancement feature map; and a verification result generation unit 344, which is used to determine whether the fingerprint authentication is passed based on the pixel difference between the defogging fingerprint image and the authorized fingerprint image.

[0033] Specifically, the fingerprint image multi-scale feature extraction unit 341 is used to perform multi-scale feature extraction on the fingerprint image to obtain a fingerprint shallow feature map and a fingerprint structural feature map. In a specific example of the present application, the fingerprint image is input into a multi-scale fingerprint feature scanner based on a dilute pyramid network to obtain the fingerprint shallow feature map and the fingerprint structural feature map. Considering that the fingerprint image contains rich feature information, there are both shallow features with rich details (such as local patterns of ridges and valleys, breakpoints, bifurcation points, etc.) and deep features that reflect the global structure of the fingerprint (such as overall texture patterns, direction fields, etc.). Therefore, in order to fully capture the multi-level features of the fingerprint image, the present application uses a multi-scale fingerprint feature scanner based on a dilute pyramid network to process the fingerprint image, and uses dilute convolution kernels of different scales to extract feature information of different levels of the fingerprint image layer by layer to capture the shallow detail features and global structural features in the fingerprint image, and generate a fingerprint shallow feature map and a fingerprint structural feature map. By comprehensively utilizing the multi-scale feature information of the fingerprint image, the adaptability of the model to fingerprint images of different qualities can be effectively enhanced, thereby more accurately performing defogging and reconstruction of the fingerprint image.

[0034] Specifically, the feature selection and enhancement unit 342 is used to perform feature selection and enhancement processing on the fingerprint shallow feature map and the fingerprint structural feature map respectively to obtain a fingerprint shallow enhanced feature map and a fingerprint structural enhanced feature map. In a specific example of the present application, the fingerprint shallow feature map and the fingerprint structural feature map are input into a feature enhancement module based on the characteristic energy continuous distribution characteristics to obtain the fingerprint shallow enhanced feature map and the fingerprint structural enhanced feature map. Taking into account that in the actual acquisition process of the fingerprint image, there may be various noise interferences, such as the shot noise introduced by the fingerprint acquisition device, the low-frequency noise caused by uneven illumination, and the additional texture noise that appears in the image due to the different finger placement positions, angles and pressures of the user when collecting the fingerprint. Therefore, in order to further improve the accuracy of fingerprint image defogging and reconstruction, the present application introduces a feature enhancement module based on the characteristic energy continuous distribution characteristics to perform feature selection and enhancement processing on the fingerprint shallow feature map and the fingerprint structural feature map respectively.

[0035] In an embodiment of the present application, the fingerprint shallow feature map and the fingerprint structure feature map are input into a feature enhancement module based on the characteristic energy continuous distribution characteristic to obtain the fingerprint shallow enhanced feature map and the fingerprint structure enhanced feature map, including: first, splitting the fingerprint shallow feature map into a set of fingerprint shallow local feature vectors; then, calculating the characteristic energy distribution spectrum vector of each fingerprint shallow local feature vector in the set of fingerprint shallow local feature vectors to obtain a set of fingerprint shallow feature energy distribution spectrum vectors; by calculating the characteristic energy distribution spectrum vector of each local feature vector, the distribution of each local feature vector at different energy levels is revealed; then, the positional mean vector of the set of fingerprint shallow feature energy distribution spectrum vectors is calculated as the energy spectrum distribution center vector; here, the positional mean of the characteristic energy distribution spectrum vector of each local feature vector is calculated to reveal the overall energy distribution trend of the feature map and obtain the energy spectrum distribution center vector; and then, the energy is calculated. The Mahalanobis distance between the spectral distribution center vector and each fingerprint shallow feature energy distribution spectrum vector in the set of fingerprint shallow feature energy distribution spectrum vectors is used as the energy distribution spectrum span factor to obtain a set of energy distribution spectrum span factors; that is, by calculating the difference between the energy spectrum distribution center vector and each feature energy distribution spectrum vector, the closeness of each local feature vector to the overall distribution is evaluated, and the corresponding energy distribution spectrum span factor is generated; further, based on the comparison between each energy distribution spectrum span factor in the set of energy distribution spectrum span factors and a preset threshold, the fingerprint shallow local feature vectors corresponding to the energy distribution spectrum span factor less than the preset threshold are selected to form a set of selected fingerprint shallow local feature vectors; in this way, redundant information deviating from the overall distribution is removed, and useful features close to the overall distribution are retained; then the set of selected fingerprint shallow local feature vectors is trans-aggregated in a feature splitting manner to obtain the fingerprint shallow enhanced feature map.

[0036] Among them, calculating the characteristic energy distribution spectrum vector of each fingerprint shallow local feature vector in the set of fingerprint shallow local feature vectors to obtain the set of fingerprint shallow local feature vectors, including: calculating the characteristic distribution energy synergy representation vector between the i-th fingerprint shallow local feature vector and each other fingerprint shallow local feature vector in the set of fingerprint shallow local feature vectors in a manner of taking the maximum eigenvalue element by element to obtain a set of characteristic distribution energy synergy representation vectors; calculating the characteristic distribution energy synergy factor of each characteristic distribution energy synergy representation vector in the set of characteristic distribution energy synergy representation vectors to obtain the characteristic energy distribution spectrum vector of the i-th fingerprint shallow local feature vector composed of multiple characteristic distribution energy synergy factors. More specifically, the process of calculating the feature distribution energy synergy factor of each feature distribution energy synergy representation vector in the set of feature distribution energy synergy representation vectors to obtain the feature energy distribution spectrum vector of the i-th fingerprint shallow local feature vector composed of multiple feature distribution energy synergy factors includes: calculating the square of the difference between the maximum eigenvalue of the feature distribution energy synergy representation vector and its feature mean, the feature variance of the feature distribution energy synergy representation vector, and the weighted sum of the bias terms, and then adding the variance of the feature distribution energy synergy representation vector and the bias term and dividing by the weighted sum to obtain the feature distribution energy synergy factor.

[0037] In summary, in the above embodiment, the fingerprint shallow feature map and the fingerprint structural feature map are respectively subjected to feature selection and enhancement processing to obtain a fingerprint shallow enhanced feature map and a fingerprint structural enhanced feature map, including: processing the set of fingerprint shallow local feature vectors using the following feature selection optimization formula to obtain the set of selected fingerprint shallow local feature vectors, wherein the feature selection optimization formula is:

[0038] X i，j =max(X i , X j )

[0039]

[0040] P i ={e i，1 , e i，2 ,...,e i，m}

[0041]

[0042]

[0043] X′={V i |Dist(c,P i )≤θ}

[0044] Among them, X i and X j They represent the i-th and j-th shallow local feature vectors in the set of shallow local feature vectors of the fingerprint respectively, max(·, ·) represents the maximum eigenvalue of the two feature vectors element by element, X i，j Represents the feature distribution energy synergy representation vector between the i-th fingerprint shallow local feature vector and the j-th fingerprint shallow local feature vector, μ, σ 2 and max(V i，j ) represent the characteristic mean, characteristic variance and maximum eigenvalue of the feature distribution energy collaborative representation vector, ε is the bias term, e i，1 、e i，2 、e i，j and e i，m Respectively represent the characteristic distribution energy synergy factors between the i-th fingerprint shallow local feature vector and the first, second, j-th and m-th fingerprint shallow local feature vectors in the set of the fingerprint shallow local feature vectors, where the value of m is the number of the fingerprint shallow local feature vectors, P i represents the energy distribution spectrum vector of the shallow feature of the i-th fingerprint, P represents the set of energy distribution spectrum vectors of the shallow feature of the fingerprint, c represents the center vector of the energy spectrum distribution, S i，c represents the covariance matrix between the energy distribution spectrum vector of the shallow feature of the i-th fingerprint and the center vector of the energy spectrum distribution, Dist(·, ·) represents the distance metric function, D i represents the span factor of the i-th energy distribution spectrum, θ represents the preset threshold, and X′ represents the set of selected shallow local feature vectors of the fingerprint.

[0045] Specifically, the defogging fingerprint image generation unit 343 is used to generate a defogging fingerprint image based on the joint features of the fingerprint shallow enhancement feature map and the fingerprint structural enhancement feature map. In a specific example of the present application, the fingerprint shallow enhancement feature map and the fingerprint structural enhancement feature map are first input into an attention-driven feature joint saliency perception network to obtain a fingerprint shallow-structural saliency joint perception feature map; then the fingerprint shallow-structural saliency joint perception feature map is input into a fingerprint defogging device based on a diffusion model to obtain the defogging fingerprint image. It should be understood that the fingerprint shallow enhancement feature map mainly describes the basic texture and edge details of the fingerprint image, while the fingerprint structural enhancement feature map focuses more on describing the overall structure and directional field information of the fingerprint image. In order to combine the advantages of both and further improve the defogging effect of the fingerprint image, the present application introduces an attention-driven feature joint saliency perception network to perform feature joint processing on the fingerprint shallow enhancement feature map and the fingerprint structural enhancement feature map to form a more comprehensive and rich fingerprint feature representation. Specifically, the network first reshapes the fingerprint shallow-enhanced feature map and the fingerprint structural-enhanced feature map into feature matrices, adapting their dimensions and shape to the input requirements of the attention mechanism. Next, by calculating the bidirectional dependencies between the two feature matrices and quantifying the interactions between features, it generates fingerprint structural-shallow feature and fingerprint shallow-structural feature dependency matrices, which serve as guidance for feature attention optimization and fusion. Furthermore, to reduce the model's sensitivity to noise and prevent overfitting, the two dependency matrices are pruned to enhance the model's generalization by selectively ignoring or suppressing some feature interactions. Furthermore, based on the pruned dependency matrices, the reshaped feature matrices are weighted optimized. Finally, through reshaping and weighted fusion, the reshaped feature matrices are restored to their original form to generate a fingerprint shallow-structural salient joint perceptual feature map. This dual attention interaction mechanism effectively exploits the correlation dependencies between shallow and structural features in fingerprint images, achieving in-depth fusion of multi-scale features and further improving fingerprint image dehazing. Furthermore, the fingerprint shallow-structural significant joint perception feature map is input into a fingerprint dehazer based on a diffusion model for image dehazing and reconstruction. Those skilled in the art should know that a diffusion model is a mathematical model based on a physical diffusion process, which can generate images by simulating a diffusion process from a high entropy state to a low entropy state. In the technical solution of the present application, the diffusion model is based on the fingerprint feature information in the fingerprint shallow-structural significant joint perception feature map, and gradually adjusts the pixel values ​​in the feature map through an iterative optimization algorithm to remove noise and blur, restore image details, and retain and enhance the key information in the original feature map, thereby generating a clear dehazed fingerprint image.

[0046] The method comprises the following steps: inputting the fingerprint shallow enhancement feature map and the fingerprint structural enhancement feature map into a feature joint saliency perception network based on attention driving to obtain a fingerprint shallow-structural saliency joint perception feature map; and inputting the fingerprint shallow-structural saliency joint perception feature map into a fingerprint dehazer based on a diffusion model to obtain the dehazed fingerprint image. The process of inputting the fingerprint shallow enhancement feature map and the fingerprint structure enhancement feature map into the attention-driven feature joint saliency perception network to obtain the fingerprint shallow-structure saliency joint perception feature map includes: reshaping the fingerprint shallow enhancement feature map and the fingerprint structure enhancement feature map to obtain the fingerprint shallow enhancement feature shape reshaping matrix and the fingerprint structure enhancement feature shape reshaping matrix; inputting the fingerprint shallow enhancement feature shape reshaping matrix and the fingerprint structure enhancement feature shape reshaping matrix into the feature channel-by-channel interactive perception module to obtain the fingerprint shallow-structure feature dependency matrix and the fingerprint structure-shallow feature dependency matrix; inputting the fingerprint shallow-structure feature dependency matrix and the fingerprint structure-shallow feature dependency matrix into the random inactivation module to obtain the pruned fingerprint shallow-structure feature dependency matrix. The invention relates to a method for obtaining a dependency relationship optimized fingerprint structure enhancement feature matrix by multiplying the pruned fingerprint structure-shallow feature dependency matrix with the fingerprint structure enhancement feature shape reshaping matrix; multiplying the pruned fingerprint structure-shallow feature dependency matrix with the fingerprint shallow enhancement feature shape reshaping matrix to obtain a dependency optimized fingerprint shallow enhancement feature matrix; reshaping the dependency optimized fingerprint shallow enhancement feature matrix and the dependency optimized fingerprint structure enhancement feature matrix to obtain an optimized fingerprint shallow enhancement feature map and an optimized fingerprint structure enhancement feature map; calculating the weighted sum of the optimized fingerprint shallow enhancement feature map and the optimized fingerprint structure enhancement feature map to obtain the fingerprint shallow-structure significant joint perception feature map.More specifically, the process of inputting the fingerprint shallow enhancement feature shape reshaping matrix and the fingerprint structure enhancement feature shape reshaping matrix into the feature channel-by-channel interactive perception module to obtain the fingerprint shallow-structural feature dependency matrix and the fingerprint structure-shallow feature dependency matrix includes: calculating the fingerprint shallow enhancement feature shape reshaping matrix and multiplying it by the transposed matrix of the fingerprint structure enhancement feature shape reshaping matrix to obtain the fingerprint shallow-structural feature association representation matrix; dividing the fingerprint shallow-structural feature association representation matrix by the scale of the fingerprint structure enhancement feature shape reshaping matrix and then inputting it into the softmax function to obtain the fingerprint shallow-structural feature dependency matrix; calculating the transposed matrix of the fingerprint shallow enhancement feature shape reshaping matrix and multiplying it by the fingerprint structure enhancement feature shape reshaping matrix to obtain the fingerprint structure-shallow feature association representation matrix; dividing the fingerprint structure-shallow feature association representation matrix by the scale of the fingerprint shallow enhancement feature shape reshaping matrix and then inputting it into the softmax function to obtain the fingerprint structure-shallow feature dependency matrix.

[0047] More specifically, the fingerprint shallow enhancement feature map and the fingerprint structural enhancement feature map are input into an attention-driven feature joint saliency perception network to obtain a fingerprint shallow-structural saliency joint perception feature map, including: processing the fingerprint shallow enhancement feature map and the fingerprint structural enhancement feature map using the following joint perception formula to obtain the fingerprint shallow-structural saliency joint perception feature map, wherein the joint perception formula is:

[0048] M1=Reshape(F1)

[0049] M2=Reshape(F2)

[0050]

[0051]

[0052] W 21 =Dropout(M 21 )

[0053] W 12 =Dropout(M 12 )

[0054]

[0055]

[0056] F1'=Reshape(M1′)

[0057] F2'=Reshape(M2')

[0058] F c =αF1'+βF2'

[0059] Wherein, F1 represents the fingerprint shallow layer enhancement feature map, F2 represents the fingerprint structure enhancement feature map, reshape(·) represents feature shape reshaping, M1 represents the fingerprint shallow layer enhancement feature shape reshaping matrix, M2 represents the fingerprint structure enhancement feature shape reshaping matrix, represents matrix multiplication, (·) T Represents the transpose of the matrix, S represents the scale of the fingerprint shallow enhancement feature shape reshaping matrix, that is, the width of the fingerprint shallow enhancement feature shape reshaping matrix multiplied by the height, and the scale of the fingerprint shallow enhancement feature shape reshaping matrix and the fingerprint structure enhancement feature shape reshaping matrix are the same, Softmax is a normalized exponential function, M 21 Represents the fingerprint shallow-structural feature dependency matrix, M 12 represents the fingerprint structure-shallow feature dependency matrix, Dropout(·) represents random inactivation processing, W 21 represents the pruned fingerprint shallow-structural feature dependency matrix, W 12 represents the pruned fingerprint structure-shallow feature dependency matrix, M1′ represents the dependency optimized fingerprint shallow enhanced feature matrix, M2′ represents the dependency optimized fingerprint structure enhanced feature matrix, F1′ represents the optimized fingerprint shallow enhanced feature map, F2′ represents the optimized fingerprint structure enhanced feature map, α and β represent different weight coefficients respectively, F c Represents the shallow-structural significant joint perceptual feature map of the fingerprint.

[0060] Specifically, the verification result generation unit 344 is used to determine whether the fingerprint authentication is passed based on the pixel difference between the defogging fingerprint image and the authorized fingerprint image. In a specific example of the present application, the fingerprint pixel-by-pixel difference matrix between the defogging fingerprint image and the authorized fingerprint image is first calculated; then the fingerprint pixel-by-pixel difference matrix is ​​input into the verification result generator based on the classifier to obtain a second-layer verification result, and the second-layer verification result is used to indicate whether the fingerprint authentication is passed. In the technical solution of the present application, the fingerprint pixel-by-pixel difference matrix between the defogging fingerprint image and the authorized fingerprint image is calculated by pixel-by-pixel comparison to reveal the pixel value difference between the defogging fingerprint image and the authorized fingerprint image at the corresponding spatial position, thereby quantitatively describing the similarity between the defogging fingerprint image and the authorized fingerprint image. Then, based on the difference information in the fingerprint pixel-by-pixel difference matrix, the fingerprint pixel-by-pixel difference matrix is ​​input into the verification result generator based on the classifier for classification judgment to obtain the second-layer verification result, and the second-layer verification result is used to indicate whether the fingerprint authentication is passed.

[0061] In a preferred example, inputting the fingerprint shallow-structural significant joint perceptual feature map into a fingerprint defogging device based on a diffusion model to obtain the defogging fingerprint image includes the following steps:

[0062] Calculating the sum of the absolute values ​​of each eigenvalue of the fingerprint shallow-structural significant joint perceptual feature map to obtain a first fingerprint shallow-structural significant joint perceptual spatial structure value, and calculating the square root of the sum of the squares of each eigenvalue of the fingerprint shallow-structural significant joint perceptual feature map to obtain a second fingerprint shallow-structural significant joint perceptual spatial structure value;

[0063] Multiplying each eigenvalue of the fingerprint shallow-structure significant joint perception feature map by the first fingerprint shallow-structure significant joint perception spatial structure value and the second fingerprint shallow-structure significant joint perception spatial structure value to obtain a first fingerprint shallow-structure significant joint perception structure reference value and a second fingerprint shallow-structure significant joint perception structure reference value corresponding to each eigenvalue;

[0064] Multiplying each eigenvalue of the fingerprint shallow-structure significant joint perceptual feature map by the scale of the fingerprint shallow-structure significant joint perceptual feature map and the square root of the scale to obtain a first fingerprint shallow-structure significant joint perceptual scale transformation value and a second fingerprint shallow-structure significant joint perceptual scale transformation value corresponding to each eigenvalue;

[0065] Dividing the first fingerprint shallow-structure significant joint perception structure reference value by the difference between the first fingerprint shallow-structure significant joint perception spatial structure value and the first fingerprint shallow-structure significant joint perception scale transformation value to obtain the first fingerprint shallow-structure significant joint perception transformation adjustment value;

[0066] Dividing the second fingerprint shallow-structure significant joint perception structure reference value by the difference between the second fingerprint shallow-structure significant joint perception spatial structure value and the second fingerprint shallow-structure significant joint perception scale transformation value to obtain the second fingerprint shallow-structure significant joint perception transformation adjustment value;

[0067] Calculating a weighted sum of the first fingerprint shallow-structural significant joint perceptual transformation adjustment value and the second fingerprint shallow-structural significant joint perceptual transformation adjustment value to obtain each eigenvalue of the optimized fingerprint shallow-structural significant joint perceptual feature map;

[0068] The optimized fingerprint shallow-structural significant joint perceptual feature map is input into the fingerprint defogging device based on the diffusion model to obtain the defogging fingerprint image.

[0069] Here, the optimized representation of the fingerprint shallow-structure significant joint perceptual feature map, denoted as F, is:

[0070]

[0071]

[0072] f 2i =(α×f i ) / (α-n×f i )

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] n=W×H×C

[0079] Where F is the fingerprint shallow-structure significant joint perceptual feature map, R represents a real number set, W, H and C represent the width, height and number of channels of the matrix respectively, n represents the scale of the fingerprint shallow-structure significant joint perceptual feature map, f i represents the eigenvalue of the ith position of the fingerprint shallow-structure significant joint perception feature map, α represents the shallow-structure significant joint perception space structure value of the first fingerprint, β represents the shallow-structure significant joint perception space structure value of the second fingerprint, ⊙ represents the point product by position, Indicates addition by position, f 1i represents the second fingerprint shallow-structure significant joint perceptual transformation adjustment value corresponding to each eigenvalue, F1 represents the second fingerprint shallow-structure significant joint perceptual transformation adjustment feature map composed of each second fingerprint shallow-structure significant joint perceptual transformation adjustment value, f 2i represents the first fingerprint shallow-structural significant joint perceptual transformation adjustment value corresponding to each eigenvalue, F2 represents the first fingerprint shallow-structural significant joint perceptual transformation adjustment feature map composed of each of the first fingerprint shallow-structural significant joint perceptual transformation adjustment values, ω represents the weighted hyperparameter, and F′ represents the optimized fingerprint shallow-structural significant joint perceptual feature map.

[0080] That is, considering that the fingerprint shallow enhancement feature map and the fingerprint structure enhancement feature map represent the image shallow enhancement semantic features and image deep structure semantic features of the fingerprint image respectively, when further performing attention-driven feature joint saliency perception, the difference in attention weights of the fusion of image shallow features and image deep structure features will cause image semantic saliency joint alignment conflicts, thereby causing the loss of aggregated key information and affecting the expression effect of the fingerprint shallow-structure saliency joint perception feature map.

[0081] Therefore, in the preferred example, with respect to the spatial structural information of the feature set of the fingerprint shallow-structural significant joint perceptual feature map in a high-dimensional space, a scale-based box transformation of each eigenvalue of the fingerprint shallow-structural significant joint perceptual feature map is performed using the class-norm spatial structured representation of the fingerprint shallow-structural significant joint perceptual feature map as a reference window, and a spatial structure-based box attention weight adjustment of each eigenvalue of the fingerprint shallow-structural significant joint perceptual feature map is implemented to ensure the invariance of spatial transformations (translation, scaling, and rotation) of the fingerprint shallow-structural significant joint perceptual feature map under feature space interaction. This thereby constructs a robust alignment guardrail between the optimized fingerprint shallow-structural significant joint perceptual feature map and the original feature set as a whole based on the aggregated features, preventing the loss of aggregated key information. In this way, the convergence and generalization effects of generative regression of the feature set of the fingerprint shallow-structural significant joint perceptual feature map under complex spatial structure representation are improved, and the image quality of the distortion-corrected aligned assembly grayscale image obtained by inputting the distortion correction module based on the diffusion model is improved.

[0082] Specifically, the access request receiving module 350 and the access permission verification module 360 ​​are configured to receive an access request input by the user after the second-layer verification is passed, and to verify the user's access permissions according to the RBAC policy. In a specific example of the present application, after the second-layer verification is passed, the RBAC policy (role-based access control policy) is used to verify whether the access request input by the user is consistent with the user's access permissions, thereby ensuring that the user can only access authorized resources. For example, finance personnel can only access the finance system, and human resources personnel can only access the human resources system.

[0083] As described above, the multi-level network identity authentication and access control system 300 according to the embodiments of the present application can be implemented in various wireless terminals, such as a server equipped with a multi-level network identity authentication and access control algorithm. In one possible implementation, the multi-level network identity authentication and access control system 300 according to the embodiments of the present application can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the multi-level network identity authentication and access control system 300 can be a software module within the operating system of the wireless terminal, or an application developed specifically for the wireless terminal. Of course, the multi-level network identity authentication and access control system 300 can also be one of the many hardware modules of the wireless terminal.

[0084] Alternatively, in another example, the multi-level network identity authentication and access control system 300 and the wireless terminal may also be separate devices, and the multi-level network identity authentication and access control system 300 may be connected to the wireless terminal via a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0085] While various embodiments of the present disclosure have been described above, the above descriptions are illustrative, non-exhaustive, and not intended to be limiting of the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or improvements to existing technologies, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A multi-level network identity authentication and access control system, characterized in that: include: User account information acquisition module, used to obtain the username and password entered by the user; A user account verification module, configured to perform a first level of verification based on the user name and password; A user fingerprint receiving module is used to receive a fingerprint image input by the user after the first layer verification is passed; A user fingerprint verification module, configured to perform a second level of verification based on the fingerprint image; an access request receiving module, configured to receive an access request input by the user after the second layer verification is passed; An access rights verification module, configured to verify the user's access rights according to the RBAC policy; The user fingerprint verification module includes: A fingerprint image multi-scale feature extraction unit, configured to perform multi-scale feature extraction on the fingerprint image to obtain a fingerprint shallow feature map and a fingerprint structure feature map; A feature selection and enhancement unit, configured to perform feature selection and enhancement processing on the fingerprint shallow feature map and the fingerprint structure feature map respectively to obtain a fingerprint shallow layer enhanced feature map and a fingerprint structure enhanced feature map; a defogging fingerprint image generating unit, configured to generate a defogging fingerprint image based on the combined features of the fingerprint shallow layer enhancement feature map and the fingerprint structure enhancement feature map; a verification result generating unit, configured to determine whether fingerprint authentication is passed based on a pixel difference between the defogging fingerprint image and the authorized fingerprint image; The feature selection and enhancement unit includes: A feature splitting subunit, configured to split the fingerprint shallow feature map into a set of fingerprint shallow local feature vectors; an energy distribution spectrum calculation subunit, configured to calculate a characteristic energy distribution spectrum vector of each fingerprint shallow local feature vector in the set of fingerprint shallow local feature vectors to obtain a set of fingerprint shallow feature energy distribution spectrum vectors; An energy spectrum distribution center calculation subunit, configured to calculate a positional mean vector of a set of energy distribution spectrum vectors of the fingerprint shallow features as an energy spectrum distribution center vector; an energy distribution spectrum span calculation subunit, configured to calculate the Mahalanobis distance between the energy spectrum distribution center vector and each fingerprint shallow feature energy distribution spectrum vector in the set of fingerprint shallow feature energy distribution spectrum vectors as an energy distribution spectrum span factor to obtain a set of energy distribution spectrum span factors; a feature screening subunit, configured to select, based on a comparison between each energy distribution spectrum span factor in the set of energy distribution spectrum span factors and a preset threshold, fingerprint shallow local feature vectors corresponding to energy distribution spectrum span factors that are smaller than the preset threshold to form a set of selected fingerprint shallow local feature vectors; The feature aggregation subunit is used to perform reverse aggregation on the set of selected fingerprint shallow local feature vectors in a feature splitting manner to obtain the fingerprint shallow enhanced feature map.

2. The multi-level network identity authentication and access control system according to claim 1, characterized in that: The fingerprint image multi-scale feature extraction unit is used to: The fingerprint image is input into a multi-scale fingerprint feature scanner based on a dilute pyramid network to obtain the fingerprint shallow feature map and the fingerprint structure feature map.

3. The multi-level network identity authentication and access control system according to claim 2, characterized in that: The feature selection and strengthening unit is used to: The fingerprint shallow feature map and the fingerprint structure feature map are input into a feature enhancement module based on the characteristic of continuous distribution of feature energy to obtain the fingerprint shallow enhanced feature map and the fingerprint structure enhanced feature map.

4. The multi-level network identity authentication and access control system according to claim 3, characterized in that: The energy distribution spectrum calculation subunit includes: The feature distribution collaborative representation secondary subunit is used to calculate the feature distribution energy collaborative representation vector between the i-th fingerprint shallow local feature vector and each other fingerprint shallow local feature vector in the set of fingerprint shallow local feature vectors in a manner of taking the maximum eigenvalue element by element to obtain a set of feature distribution energy collaborative representation vectors; The feature distribution energy synergy factor calculation secondary subunit is used to calculate the feature distribution energy synergy factor of each feature distribution energy synergy representation vector in the set of feature distribution energy synergy representation vectors to obtain the feature energy distribution spectrum vector of the i-th fingerprint shallow local feature vector composed of multiple feature distribution energy synergy factors.

5. The multi-level network identity authentication and access control system according to claim 4, characterized in that: The characteristic distribution energy synergy factor calculation secondary subunit is used to: Calculate the square of the difference between the maximum eigenvalue of the feature distribution energy synergistic representation vector and its eigenmean, the feature variance of the feature distribution energy synergistic representation vector, and the weighted sum of the bias terms, then add the variance of the feature distribution energy synergistic representation vector and the bias term and divide by the weighted sum to obtain the feature distribution energy synergy factor.

6. The multi-level network identity authentication and access control system according to claim 5, characterized in that: The defogging fingerprint image generation unit includes: A feature joint perception subunit, configured to input the fingerprint shallow layer enhanced feature map and the fingerprint structural enhanced feature map into an attention-driven feature joint saliency perception network to obtain a fingerprint shallow layer-structural saliency joint perception feature map; The fingerprint defogging subunit is used to input the fingerprint shallow-structural significant joint perception feature map into a fingerprint defogging device based on a diffusion model to obtain the defogging fingerprint image.

7. The multi-level network identity authentication and access control system according to claim 6, characterized in that: The feature joint perception subunit includes: A first feature shape reshaping secondary subunit is configured to perform feature shape reshaping on the fingerprint shallow layer enhancement feature map and the fingerprint structure enhancement feature map to obtain a fingerprint shallow layer enhancement feature shape reshaping matrix and a fingerprint structure enhancement feature shape reshaping matrix; a bidirectional dependency calculation secondary subunit, configured to input the fingerprint shallow enhancement feature shape reshaping matrix and the fingerprint structural enhancement feature shape reshaping matrix into a feature channel-by-channel interactive perception module to obtain a fingerprint shallow-structural feature dependency matrix and a fingerprint structural-shallow feature dependency matrix; A random dropout secondary subunit, configured to input the fingerprint shallow-structural feature dependency matrix and the fingerprint structure-shallow feature dependency matrix into a random dropout module to obtain a pruned fingerprint shallow-structural feature dependency matrix and a pruned fingerprint structure-shallow feature dependency matrix; a structural feature dependency optimization secondary subunit, configured to perform matrix multiplication on the pruned fingerprint shallow-structural feature dependency matrix and the fingerprint structure enhancement feature shape reshaping matrix to obtain a dependency optimization fingerprint structure enhancement feature matrix; A shallow feature dependency optimization secondary subunit is used to perform matrix multiplication on the pruned fingerprint structure-shallow feature dependency matrix and the fingerprint shallow enhancement feature shape reshaping matrix to obtain a dependency optimized fingerprint shallow enhancement feature matrix; The second feature shape reshaping secondary subunit is used to perform feature shape reshaping on the dependency optimized fingerprint shallow layer enhancement feature matrix and the dependency optimized fingerprint structure enhancement feature matrix to obtain an optimized fingerprint shallow layer enhancement feature map and an optimized fingerprint structure enhancement feature map; The multi-scale joint secondary subunit is used to calculate the weighted sum of the optimized fingerprint shallow enhancement feature map and the optimized fingerprint structure enhancement feature map to obtain the fingerprint shallow-structure significant joint perceptual feature map.

8. The multi-level network identity authentication and access control system according to claim 7, characterized in that: The bidirectional dependency calculation secondary subunit is used to: Calculating the transposed matrix of the fingerprint shallow layer enhancement feature shape reshaping matrix multiplied by the fingerprint structural enhancement feature shape reshaping matrix to obtain a fingerprint shallow layer-structural feature association representation matrix; Dividing the fingerprint shallow-structural feature association representation matrix by the scale of the fingerprint structural enhancement feature shape reshaping matrix and then inputting the matrix into the softmax function to obtain the fingerprint shallow-structural feature dependency matrix; Calculating the transposed matrix of the fingerprint shallow layer enhanced feature shape reshaping matrix and multiplying it by the fingerprint structure enhanced feature shape reshaping matrix to obtain a fingerprint structure-shallow layer feature association representation matrix; The fingerprint structure-shallow feature association representation matrix is ​​divided by the scale of the fingerprint shallow enhanced feature shape reshaping matrix and then input into the softmax function to obtain the fingerprint structure-shallow feature dependency matrix.

9. The multi-level network identity authentication and access control system according to claim 8, characterized in that: The verification result generating unit includes: A difference calculation subunit, configured to calculate a fingerprint pixel-by-pixel difference matrix between the defogging fingerprint image and the authorized fingerprint image; The fingerprint authentication subunit is used to input the fingerprint pixel-by-pixel difference matrix into the verification result generator based on the classifier to obtain a second-level verification result, where the second-level verification result is used to indicate whether the fingerprint authentication is passed.

Images