CN116721441B - Block chain-based access control security management method and system - Google Patents
Block chain-based access control security management method and system Download PDFInfo
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1365—Matching; Classification
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
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- G07C9/00563—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys using personal physical data of the operator, e.g. finger prints, retinal images, voicepatterns
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Abstract
A method and system for managing entrance guard safety based on block chain are disclosed. Firstly, carrying out image preprocessing on a fingerprint image to be authenticated to obtain a preprocessed fingerprint image to be authenticated, then, passing the preprocessed fingerprint image to be authenticated and an input fingerprint image of a first identity tag through a twin detection model to obtain a fingerprint feature image to be authenticated and an input fingerprint feature image, then, calculating a transfer matrix between feature matrixes of each group of corresponding channel dimensions of the fingerprint feature image to be authenticated and the input fingerprint feature image to obtain a transfer feature image composed of a plurality of transfer matrixes, and finally, optimizing the transfer feature image and then, passing through a classifier to obtain a classification result for indicating whether the fingerprint image to be authenticated belongs to the first identity tag. Therefore, the problem that matching accuracy is reduced due to the fact that the input fingerprint gesture and the abnormal fingerprint surface state can be solved, and accuracy of the access control system is improved.
Description
Technical Field
The present application relates to the field of access security management, and more particularly, to a blockchain-based access security management method and system.
Background
The block chain is a decentralization distributed technology, takes blocks as basic units, links a plurality of blocks together to form a non-tamperable chain structure, and has important application value in the aspect of entrance guard security management. That is, the blockchain can ensure that the data has non-falsifiability, and can prevent the background data from being falsified to influence the security of the access control.
However, when the door control system collects the fingerprint image to be authenticated, when the gesture of the input fingerprint and the surface state of the fingerprint are abnormal, for example, the finger is wetted, the matching accuracy between the input fingerprint image and the fingerprint image to be authenticated is reduced, and the operation of the door control system is affected.
Thus, an optimized blockchain-based access security management scheme is desired.
Disclosure of Invention
In view of the above, the disclosure provides a blockchain-based access control security management method and system, which can solve the problem of reduced matching accuracy caused by abnormal input fingerprint gestures and fingerprint surface states, and improve the accuracy of an access control system.
According to an aspect of the present disclosure, there is provided a blockchain-based access security management method, including:
downloading an input fingerprint image of the first identity tag from the blockchain structure;
Acquiring a fingerprint image to be authenticated, which is acquired by access control equipment;
performing image preprocessing on the fingerprint image to be authenticated to obtain a preprocessed fingerprint image to be authenticated;
the fingerprint image to be authenticated after pretreatment and the input fingerprint image of the first identity tag are processed through a twin detection model comprising a first convolutional neural network model and a second convolutional neural network model to obtain a fingerprint feature map to be authenticated and an input fingerprint feature map;
calculating transfer matrixes between the fingerprint feature images to be authenticated and feature matrixes of each group of corresponding channel dimensions of the input fingerprint feature images to obtain a transfer feature image composed of a plurality of transfer matrixes;
optimizing the transfer characteristic diagram to obtain an optimized transfer characteristic diagram; and
and the optimized transfer feature map passes through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the fingerprint image to be authenticated belongs to a first identity tag.
In one possible implementation, the first convolutional neural network model includes an input layer, a first convolutional layer, a first active layer, a first pooling layer, a second convolutional layer, a second active layer, a second pooling layer, and an output layer.
In one possible implementation, the first convolution layer uses 64 convolution kernels of 3×3 size with a step size of 1 and padding of 1, and the second convolution layer uses 128 convolution kernels of 3×3 size with a step size of 1 and padding of 1.
In one possible implementation, the first pooling layer uses a 2×2 pooling kernel with a step size of 2.
In one possible implementation manner, the pre-processing fingerprint image to be authenticated and the input fingerprint image of the first identity tag are passed through a twin detection model including a first convolutional neural network model and a second convolutional neural network model to obtain a fingerprint feature map to be authenticated and an input fingerprint feature map, including:
passing the preprocessed fingerprint image to be authenticated through the first convolutional neural network model of the twin detection model to obtain the fingerprint feature map to be authenticated; and
and passing the recorded fingerprint image of the first identity tag through the second convolution neural network model of the twin detection model to obtain the recorded fingerprint feature map.
In one possible implementation, calculating a transfer matrix between the feature matrices of each set of corresponding channel dimensions of the fingerprint feature map to be authenticated and the input fingerprint feature map to obtain a transfer feature map composed of a plurality of transfer matrices, includes:
Calculating transfer matrixes among the feature matrixes of each group of corresponding channel dimensions of the fingerprint feature graphs to be authenticated and the input fingerprint feature graphs according to the following transfer matrix calculation formula to obtain a plurality of transfer matrixes;
the calculation formula of the transfer matrix is as follows:
wherein,andrespectively representing the feature matrix of each group of corresponding channel dimensions of the fingerprint feature map to be authenticated and the input fingerprint feature map,the transfer matrix is represented by a matrix of the transfer,representing matrix multiplication;
and arranging the plurality of transfer matrixes to obtain the transfer characteristic diagram.
In one possible implementation, optimizing the transfer feature map to obtain an optimized transfer feature map includes:
calculating quantized transferable sensing factors of each feature matrix of the transfer feature map relative to transferable features of the transfer feature map to obtain a plurality of transferable sensing factors; and
each feature matrix of the transfer feature map is weighted with the plurality of transferable sensing factors to obtain the optimized transfer feature map.
In one possible implementation, calculating quantized transferable sensing factors for each feature matrix of the transfer feature map relative to transferable features of the transfer feature map to obtain a plurality of transferable sensing factors comprises:
Calculating quantized transferable sensing factors of each feature matrix of the transfer feature map relative to transferable features of the transfer feature map by factors to obtain a plurality of transferable sensing factors;
wherein, the factor calculation formula is:
wherein,is the first of the transfer characteristic diagramsThe number of feature matrices is chosen such that,respectively the firstThe first feature matrixThe value of the characteristic is a value of,is a graph of the transfer characteristics,is the first of the transfer characteristic diagramsThe value of the characteristic is a value of,is a base 2 logarithm, and alpha is a weighted hyper-parameter,is the firstA transferable sensing factor.
In one possible implementation manner, the optimizing transfer feature map is passed through a classifier to obtain a classification result, where the classification result is used to indicate whether the fingerprint image to be authenticated belongs to a first identity tag, and the method includes:
expanding the optimized transfer feature map into an optimized classification feature vector according to a row vector or a column vector;
performing full-connection coding on the optimized classification feature vector by using a full-connection layer of the classifier to obtain a coding classification feature vector; and
and inputting the coding classification feature vector into a Softmax classification function of the classifier to obtain the classification result.
According to another aspect of the present disclosure, there is provided a blockchain-based access security management system, including:
the input fingerprint image acquisition module is used for downloading an input fingerprint image of the first identity tag from the blockchain structure;
the fingerprint image acquisition module to be authenticated is used for acquiring a fingerprint image to be authenticated acquired by the access control equipment;
the image preprocessing module is used for carrying out image preprocessing on the fingerprint image to be authenticated so as to obtain a preprocessed fingerprint image to be authenticated;
the twin detection module is used for enabling the preprocessed fingerprint image to be authenticated and the input fingerprint image of the first identity tag to pass through a twin detection model comprising a first convolutional neural network model and a second convolutional neural network model to obtain a fingerprint feature map to be authenticated and an input fingerprint feature map;
the transfer module is used for calculating transfer matrixes between the fingerprint feature image to be authenticated and the feature matrixes of each group of corresponding channel dimensions of the input fingerprint feature image so as to obtain a transfer feature image composed of a plurality of transfer matrixes;
the optimizing module is used for optimizing the transfer characteristic diagram to obtain an optimized transfer characteristic diagram; and
and the classification module is used for enabling the optimized transfer feature map to pass through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the fingerprint image to be authenticated belongs to the first identity tag or not.
According to the embodiment of the disclosure, firstly, an image preprocessing is performed on a fingerprint image to be authenticated to obtain a preprocessed fingerprint image to be authenticated, then, the preprocessed fingerprint image to be authenticated and an input fingerprint image of a first identity tag are subjected to a twin detection model to obtain a fingerprint feature image to be authenticated and an input fingerprint feature image, then, a transfer matrix between feature matrices of each group of corresponding channel dimensions of the fingerprint feature image to be authenticated and the input fingerprint feature image is calculated to obtain a transfer feature image composed of a plurality of transfer matrices, and finally, the transfer feature image is optimized and then, a classifier is used for obtaining a classification result used for indicating whether the fingerprint image to be authenticated belongs to the first identity tag. Therefore, the problem that matching accuracy is reduced due to the fact that the input fingerprint gesture and the abnormal fingerprint surface state can be solved, and accuracy of the access control system is improved.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features and aspects of the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 illustrates an application scenario diagram of a blockchain-based access security management method according to an embodiment of the present disclosure.
Fig. 2 shows a flowchart of a blockchain-based access security management method in accordance with embodiments of the present disclosure.
Fig. 3 shows an architecture schematic of a blockchain-based access security management method in accordance with an embodiment of the present disclosure.
Fig. 4 shows a flowchart of sub-step S140 of a blockchain-based access security management method in accordance with an embodiment of the present disclosure.
Fig. 5 shows a flowchart of sub-step S160 of a blockchain-based access security management method in accordance with an embodiment of the present disclosure.
Fig. 6 shows a flowchart of sub-step S170 of a blockchain-based access security management method in accordance with an embodiment of the present disclosure.
Fig. 7 illustrates a block diagram of a blockchain-based access security management system in accordance with embodiments of the present disclosure.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some, but not all embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present application without making any inventive effort, are also within the scope of the present application.
As used in this application and in the claims, the terms "a," "an," "the," and/or "the" are not specific to the singular, but may include the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
Flowcharts are used in this application to describe the operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
Various exemplary embodiments, features and aspects of the disclosure will be described in detail below with reference to the drawings. In the drawings, like reference numbers indicate identical or functionally similar elements. Although various aspects of the embodiments are illustrated in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
In addition, numerous specific details are set forth in the following detailed description in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order not to obscure the present disclosure.
Fig. 1 illustrates an application scenario diagram of blockchain-based access security management in accordance with an embodiment of the present disclosure. As shown in fig. 1, in this application scenario, first, an input fingerprint image of a first identity tag (e.g., D1 illustrated in fig. 1) is downloaded from a blockchain structure (e.g., N1 illustrated in fig. 1), and a fingerprint image to be authenticated (e.g., D2 illustrated in fig. 1) acquired by an access control device (e.g., N2 illustrated in fig. 1) is acquired, and then, the fingerprint image to be authenticated and the input fingerprint image of the first identity tag are input to a server (e.g., S illustrated in fig. 1) in which a blockchain-based access control security management algorithm is deployed, wherein the server is capable of processing the fingerprint image to be authenticated and the input fingerprint image of the first identity tag using the blockchain-based access control security management algorithm to obtain a classification result indicating whether the fingerprint image to be authenticated belongs to the first identity tag.
Fig. 2 shows a flowchart of a blockchain-based access security management method in accordance with embodiments of the present disclosure. Fig. 3 shows an architecture schematic of a blockchain-based access security management method in accordance with an embodiment of the present disclosure. As shown in fig. 2 and 3, the access security management method based on the blockchain according to the embodiment of the application includes the following steps: s110, downloading an input fingerprint image of a first identity tag from a block chain structure; s120, acquiring a fingerprint image to be authenticated, which is acquired by access control equipment; s130, carrying out image preprocessing on the fingerprint image to be authenticated to obtain a preprocessed fingerprint image to be authenticated; s140, the fingerprint image to be authenticated after preprocessing and the input fingerprint image of the first identity tag are processed through a twin detection model comprising a first convolutional neural network model and a second convolutional neural network model to obtain a fingerprint feature map to be authenticated and an input fingerprint feature map; s150, calculating transfer matrixes between the fingerprint feature map to be authenticated and the feature matrixes of each group of corresponding channel dimensions of the input fingerprint feature map to obtain a transfer feature map composed of a plurality of transfer matrixes; s160, optimizing the transfer characteristic diagram to obtain an optimized transfer characteristic diagram; and S170, enabling the optimized transfer feature map to pass through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the fingerprint image to be authenticated belongs to the first identity tag.
More specifically, in step S110, the entered fingerprint image of the first identity tag is downloaded from the blockchain structure. Aiming at the technical requirements, the technical concept of the access control system is to comprehensively utilize the blockchain and the deep learning technology, extract the characteristics of the input fingerprint image and the fingerprint image to be authenticated, and compare and match in a high-dimensional characteristic space, so that the problem that the matching accuracy is reduced due to the abnormal input fingerprint gesture and fingerprint surface state is solved, and the accuracy of the access control system is improved.
Specifically, in the technical scheme of the application, firstly, downloading an input fingerprint image of a first identity tag from a block chain structure, and acquiring a fingerprint image to be authenticated, which is acquired by access control equipment. Here, the blockchain technology is adopted to store the input fingerprint image of the first identity tag so as to ensure the non-tamper property and the security of the data by utilizing the characteristics of the input fingerprint image, so that the input fingerprint image is stored on the blockchain, and the data can be effectively prevented from being tampered or lost.
More specifically, in step S120, a fingerprint image to be authenticated acquired by the access control device is acquired. That is, the fingerprint image to be authenticated is a fingerprint image collected by the user on the access control device. In a specific example of the application, the access control device adopts a technical implementation manner of optical fingerprint identification to collect fingerprint images.
More specifically, in step S130, the fingerprint image to be authenticated is subjected to image preprocessing to obtain a preprocessed fingerprint image to be authenticated. In practical applications, there may be some problems in the fingerprint image to be authenticated, such as image noise, uneven illumination, etc., which affect the accuracy and stability of fingerprint identification. Therefore, in the technical scheme of the application, the fingerprint image to be authenticated is subjected to image preprocessing so as to improve the accuracy of fingerprint identification, and the fingerprint image to be authenticated after preprocessing is obtained. Wherein the image preprocessing includes, but is not limited to: filtering, histogram equalization, binarization, edge detection.
More specifically, in step S140, the preprocessed fingerprint image to be authenticated and the input fingerprint image of the first identity tag are passed through a twin detection model including a first convolutional neural network model and a second convolutional neural network model to obtain a fingerprint feature map to be authenticated and an input fingerprint feature map. The twin detection model is a common deep learning model and consists of two convolutional neural networks with the same structure, respectively processes two input images and finally outputs a characteristic diagram of the two images. Therefore, the difference between the characteristics caused by the difference of the network model ends can be reduced, and the accuracy of comparison is improved as much as possible.
Specifically, two convolutional neural network models contained in the twin detection model respectively extract features of the fingerprint image to be authenticated and the input fingerprint image. The network structure of the first convolutional neural network model is an input layer- > convolutional layer 1- > active layer 1- > pooling layer 1- > convolutional layer 2- > active layer 2- > pooling layer 2- > output layer. That is, it includes an input layer, a first convolution layer, a first activation layer, a first pooling layer, a second convolution layer, a second activation layer, a second pooling layer, and an output layer. The input layer of the first convolutional neural network model inputs the fingerprint image to be authenticated after preprocessing, and the output layer outputs the fingerprint characteristic image to be authenticated. In a specific example of the present application, the first convolution layer uses 64 convolution kernels with a size of 3×3, a step size of 1, a padding of 1, an activation function used by the first activation layer is ReLU, the first pooling layer uses a pooling kernel with a size of 2×2, a step size of 2, the second convolution layer uses 128 convolution kernels with a size of 3×3, a step size of 1, a padding of 1, an activation function used by the second activation layer is ReLU, and the second pooling layer has the same structure as the first pooling layer. The second convolutional neural network model has the same network structure as the first convolutional neural network model. It is worth mentioning that the activation function may use sigmoid, tanh, etc. in addition to ReLU.
Accordingly, in one possible implementation manner, as shown in fig. 4, the step of passing the preprocessed fingerprint image to be authenticated and the input fingerprint image of the first identity tag through a twin detection model including a first convolutional neural network model and a second convolutional neural network model to obtain a fingerprint feature map to be authenticated and an input fingerprint feature map includes: s141, passing the preprocessed fingerprint image to be authenticated through the first convolutional neural network model of the twin detection model to obtain the fingerprint feature map to be authenticated; and S142, passing the input fingerprint image of the first identity tag through the second convolution neural network model of the twin detection model to obtain the input fingerprint feature map.
More specifically, in step S150, a transfer matrix between the feature matrices of each set of corresponding channel dimensions of the fingerprint feature map to be authenticated and the input fingerprint feature map is calculated to obtain a transfer feature map composed of a plurality of transfer matrices. The transfer matrix is used for mapping the features of the fingerprint image to be authenticated into the feature space of the input fingerprint image. Specifically, the transfer matrix can transfer the feature matrix of the fingerprint image to be authenticated and the feature matrix of the input fingerprint image in the corresponding channel dimension, so as to obtain a transfer feature map composed of a plurality of transfer matrices. This transfer profile may capture the differences between the fingerprint to be authenticated and the entered fingerprint.
Accordingly, in one possible implementation, calculating a transfer matrix between the feature matrices of each set of corresponding channel dimensions of the fingerprint feature map to be authenticated and the input fingerprint feature map to obtain a transfer feature map composed of a plurality of transfer matrices, includes: calculating transfer matrixes among the feature matrixes of each group of corresponding channel dimensions of the fingerprint feature graphs to be authenticated and the input fingerprint feature graphs according to the following transfer matrix calculation formula to obtain a plurality of transfer matrixes; the calculation formula of the transfer matrix is as follows:
wherein,andrespectively representing the feature matrix of each group of corresponding channel dimensions of the fingerprint feature map to be authenticated and the input fingerprint feature map,the transfer matrix is represented by a matrix of the transfer,representing matrix multiplication; and arranging the plurality of transfer matrixes to obtain the transfer characteristic diagram. Further, arranging the plurality of transfer matrices to obtain the transfer feature map includes: turning the plurality of turnsAnd arranging the shift matrix along the channel dimension to obtain the transfer characteristic diagram.
More specifically, in step S160, the transfer feature map is optimized to obtain an optimized transfer feature map. In the technical scheme of the application, each feature matrix in the fingerprint feature map to be authenticated and the input fingerprint feature map respectively expresses the image semantic features of the fingerprint image to be authenticated and the input fingerprint image of the first identity tag, and expresses the feature extraction channel relevance of the first convolutional neural network model and the second convolutional neural network model serving as feature extractors along the channel dimension, and considering that the source image difference of the fingerprint image to be authenticated and the input fingerprint image of the first identity tag is only extracted by independent channel dimension features in the convolutional neural network model, the transfer matrix between the feature matrices of each group of corresponding channel dimensions of the fingerprint feature map to be authenticated and the input fingerprint feature map has higher inconsistency of overall distribution, so that the transfer feature map has domain transfer difference to a fusion feature domain when being arranged along the channel dimension, and the convergence difficulty of the transfer feature map is promoted when the transfer feature map is classified and regressed by a classifier.
Thus, the applicant of the present application, for each feature matrix of the transfer feature map, for example, marks asWherein，The number of channels being the transfer profile, and the transfer profile, e.g. noted asA quantized transferable sensing factor of its transferable characteristics is calculated.
Accordingly, in one possible implementation, as shown in fig. 5, optimizing the transfer feature map to obtain an optimized transfer feature map includes: s161, calculating quantized transferable sensing factors of each feature matrix of the transfer feature map relative to transferable features of the transfer feature map to obtain a plurality of transferable sensing factors; and S162, weighting each feature matrix of the transfer feature map with the plurality of transferable sensing factors to obtain the optimized transfer feature map.
Accordingly, in one possible implementation, calculating a quantized transferable sensing factor for each feature matrix of the transfer feature map relative to transferable features of the transfer feature map to obtain a plurality of transferable sensing factors comprises:
calculating quantized transferable sensing factors of each feature matrix of the transfer feature map relative to transferable features of the transfer feature map by factors to obtain a plurality of transferable sensing factors;
Wherein, the factor calculation formula is:
wherein,is the first of the transfer characteristic diagramsThe number of feature matrices is chosen such that,respectively the firstThe first feature matrixThe value of the characteristic is a value of,is a graph of the transfer characteristics,is the first of the transfer characteristic diagramsThe value of the characteristic is a value of,is a base 2 logarithm, and alpha is a weighted hyper-parameter,is the firstA transferable sensing factor.
Here, the quantized transferable sensing factor of the transferable feature estimates the domain uncertainty of the feature space domain to the classification target domain through the uncertainty measure under the domain transfer, and since the domain uncertainty estimate can be used for identifying the feature representation transferred between domains, by weighting each transfer matrix with the factor as a weight, whether the feature map is effectively transferred between domains can be identified through the cross-domain alignment of the feature space domain to the classification target domain, thereby quantitatively sensing the transferable nature of the transferable feature in different feature vectors, so as to realize the inter-domain adaptive feature fusion, and improve the convergence effect of the transferred feature map when the classification regression is performed through the classifier, namely, improve the training speed and the accuracy of the converged classification result.
More specifically, in step S170, the optimized transfer feature map is passed through a classifier to obtain a classification result, where the classification result is used to indicate whether the fingerprint image to be authenticated belongs to the first identity tag. In this way it is determined whether the fingerprint feature map to be authenticated belongs to the first identity tag. The classifier can learn a classification rule according to the relation between the feature images and the labels in the training data, and the classification rule is used for classifying and predicting the transfer feature images input during inference, so that a classification result is obtained. In this way it is determined whether the fingerprint feature map to be authenticated belongs to the first identity tag.
That is, in the technical solution of the present application, the label of the classifier includes that the fingerprint image to be authenticated belongs to a first identity label (first label), and that the fingerprint image to be authenticated does not belong to a first identity label (second label), wherein the classifier determines, through a soft maximum function, to which classification label the optimized transfer feature map belongs. It should be noted that the first tag p1 and the second tag p2 do not include a manually set concept, and in fact, during the training process, the computer model does not have a concept of "whether the fingerprint image to be authenticated belongs to the first identity tag", which is simply that there are two kinds of classification tags and the probability that the output feature is under the two kinds of classification tags, that is, the sum of p1 and p2 is one. Therefore, the classification result of whether the fingerprint image to be authenticated belongs to the first identity tag is actually converted into the classified probability distribution conforming to the natural rule through the classified tag, and the physical meaning of the natural probability distribution of the tag is essentially used instead of the language text meaning of whether the fingerprint image to be authenticated belongs to the first identity tag.
It should be appreciated that the role of the classifier is to learn classification rules with a given class, known training data, and then classify (or predict) unknown data. Logistic regression (logistics), SVM, etc. are commonly used to solve the classification problem, and for multi-classification problems (multi-class classification), logistic regression or SVM can be used as well, but multiple bi-classifications are required to compose multiple classifications, but this is error-prone and inefficient, and the commonly used multi-classification method is the Softmax classification function.
Accordingly, in one possible implementation manner, as shown in fig. 6, the optimizing transfer feature map is passed through a classifier to obtain a classification result, where the classification result is used to indicate whether the fingerprint image to be authenticated belongs to the first identity tag, and the method includes: s171, the optimized transfer feature map is unfolded into optimized classification feature vectors according to row vectors or column vectors; s172, performing full-connection coding on the optimized classification feature vector by using a full-connection layer of the classifier to obtain a coded classification feature vector; and S173, inputting the coding classification feature vector into a Softmax classification function of the classifier to obtain the classification result.
In conclusion, according to the blockchain-based access control safety management method, the problem that matching accuracy is reduced due to the fact that the input fingerprint gesture and the abnormal fingerprint surface state are abnormal can be solved, and accuracy of an access control system is improved.
Fig. 7 shows a block diagram of a blockchain-based access security management system 100 in accordance with embodiments of the present disclosure. As shown in fig. 7, a blockchain-based access security management system 100 according to an embodiment of the present application includes: an input fingerprint image acquisition module 110 for downloading an input fingerprint image of the first identity tag from the blockchain structure; the fingerprint image to be authenticated acquisition module 120 is used for acquiring a fingerprint image to be authenticated acquired by the access control equipment; the image preprocessing module 130 is configured to perform image preprocessing on the fingerprint image to be authenticated to obtain a preprocessed fingerprint image to be authenticated; the twin detection module 140 is configured to pass the preprocessed fingerprint image to be authenticated and the input fingerprint image of the first identity tag through a twin detection model including a first convolutional neural network model and a second convolutional neural network model to obtain a fingerprint feature map to be authenticated and an input fingerprint feature map; the transfer module 150 is configured to calculate a transfer matrix between the to-be-authenticated fingerprint feature map and the feature matrices of each set of corresponding channel dimensions of the input fingerprint feature map to obtain a transfer feature map composed of a plurality of transfer matrices; an optimizing module 160, configured to optimize the transfer feature map to obtain an optimized transfer feature map; and a classification module 170, configured to pass the optimized transfer feature map through a classifier to obtain a classification result, where the classification result is used to indicate whether the fingerprint image to be authenticated belongs to the first identity tag.
Here, it will be understood by those skilled in the art that the specific functions and operations of the respective units and modules in the above-described blockchain-based access security management system 100 have been described in detail in the above description of the blockchain-based access security management method with reference to fig. 1 to 6, and thus, repetitive descriptions thereof will be omitted.
As described above, the blockchain-based access security management system 100 according to the embodiments of the present application may be implemented in various wireless terminals, such as a server or the like having a blockchain-based access security management algorithm. In one possible implementation, the blockchain-based access security management system 100 according to embodiments of the present application may be integrated into a wireless terminal as a software module and/or hardware module. For example, the blockchain-based access security management system 100 may be a software module in the operating system of the wireless terminal, or may be an application developed for the wireless terminal; of course, the blockchain-based access security management system 100 may also be one of a plurality of hardware modules of the wireless terminal.
Alternatively, in another example, the blockchain-based access security management system 100 and the wireless terminal may be separate devices, and the blockchain-based access security management system 100 may be connected to the wireless terminal through a wired and/or wireless network and communicate the interaction information in a agreed-upon data format.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as a memory including computer program instructions executable by a processing component of an apparatus to perform the above-described method.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (8)
1. The access control safety management method based on the block chain is characterized by comprising the following steps of:
downloading an input fingerprint image of the first identity tag from the blockchain structure;
acquiring a fingerprint image to be authenticated, which is acquired by access control equipment;
performing image preprocessing on the fingerprint image to be authenticated to obtain a preprocessed fingerprint image to be authenticated;
the fingerprint image to be authenticated after pretreatment and the input fingerprint image of the first identity tag are processed through a twin detection model comprising a first convolutional neural network model and a second convolutional neural network model to obtain a fingerprint feature map to be authenticated and an input fingerprint feature map;
Calculating transfer matrixes between the fingerprint feature images to be authenticated and feature matrixes of each group of corresponding channel dimensions of the input fingerprint feature images to obtain a transfer feature image composed of a plurality of transfer matrixes;
optimizing the transfer characteristic diagram to obtain an optimized transfer characteristic diagram; and
the optimized transfer feature map is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether a fingerprint image to be authenticated belongs to a first identity tag or not;
wherein optimizing the transfer feature map to obtain an optimized transfer feature map comprises:
calculating quantized transferable sensing factors of each feature matrix of the transfer feature map relative to transferable features of the transfer feature map to obtain a plurality of transferable sensing factors; and
weighting each feature matrix of the transfer feature map with the plurality of transferable sensing factors to obtain the optimized transfer feature map;
wherein calculating quantized transferable sensing factors of each feature matrix of the transfer feature map relative to transferable features of the transfer feature map to obtain a plurality of transferable sensing factors comprises:
calculating quantized transferable sensing factors of each feature matrix of the transfer feature map relative to transferable features of the transfer feature map with the following factor calculation formula to obtain a plurality of transferable sensing factors;
Wherein, the factor calculation formula is:wherein,is the first of the transfer characteristic diagramsThe number of feature matrices is chosen such that,each of which isThe first feature matrixThe value of the characteristic is a value of,is a graph of the transfer characteristics,is the first of the transfer characteristic diagramsThe value of the characteristic is a value of,is a logarithm based on 2, andis a weighted super-parameter that is used to determine the weight of the object,is the firstA transferable sensing factor.
2. The blockchain-based access security management method of claim 1, wherein the first convolutional neural network model includes an input layer, a first convolutional layer, a first active layer, a first pooling layer, a second convolutional layer, a second active layer, a second pooling layer, and an output layer.
3. The blockchain-based access security management method of claim 2, wherein the first convolution layer uses 64 convolution kernels of 3 x 3 size with a step size of 1 and padding of 1, and the second convolution layer uses 128 convolution kernels of 3 x 3 size with a step size of 1 and padding of 1.
4. The blockchain-based access security management method of claim 3, wherein the first pooling layer uses a 2 x 2 pooling core with a step size of 2.
5. The blockchain-based access security management method of claim 4, wherein passing the preprocessed fingerprint image to be authenticated and the input fingerprint image of the first identity tag through a twin detection model comprising a first convolutional neural network model and a second convolutional neural network model to obtain a fingerprint feature map to be authenticated and an input fingerprint feature map, comprises:
Passing the preprocessed fingerprint image to be authenticated through the first convolutional neural network model of the twin detection model to obtain the fingerprint feature map to be authenticated; and
and passing the recorded fingerprint image of the first identity tag through the second convolution neural network model of the twin detection model to obtain the recorded fingerprint feature map.
6. The blockchain-based access security management method of claim 5, wherein calculating a transfer matrix between feature matrices of each set of corresponding channel dimensions of the fingerprint feature map to be authenticated and the input fingerprint feature map to obtain a transfer feature map composed of a plurality of transfer matrices, comprises:
calculating transfer matrixes among the feature matrixes of each group of corresponding channel dimensions of the fingerprint feature graphs to be authenticated and the input fingerprint feature graphs according to the following transfer matrix calculation formula to obtain a plurality of transfer matrixes;
the calculation formula of the transfer matrix is as follows:wherein (1)>And->Feature matrices representing the corresponding channel dimensions of each group of the fingerprint feature map to be authenticated and the input fingerprint feature map, respectively, < >>Representing the transfer matrix->Representing matrix multiplication;
And arranging the plurality of transfer matrixes to obtain the transfer characteristic diagram.
7. The blockchain-based access security management method of claim 6, wherein the optimizing transfer feature map is passed through a classifier to obtain a classification result, the classification result being used to indicate whether a fingerprint image to be authenticated belongs to a first identity tag, and the method comprises:
expanding the optimized transfer feature map into an optimized classification feature vector according to a row vector or a column vector;
performing full-connection coding on the optimized classification feature vector by using a full-connection layer of the classifier to obtain a coding classification feature vector; and
and inputting the coding classification feature vector into a Softmax classification function of the classifier to obtain the classification result.
8. An access security management system based on a blockchain, comprising:
the input fingerprint image acquisition module is used for downloading an input fingerprint image of the first identity tag from the blockchain structure;
the fingerprint image acquisition module to be authenticated is used for acquiring a fingerprint image to be authenticated acquired by the access control equipment;
the image preprocessing module is used for carrying out image preprocessing on the fingerprint image to be authenticated so as to obtain a preprocessed fingerprint image to be authenticated;
The twin detection module is used for enabling the preprocessed fingerprint image to be authenticated and the input fingerprint image of the first identity tag to pass through a twin detection model comprising a first convolutional neural network model and a second convolutional neural network model to obtain a fingerprint feature map to be authenticated and an input fingerprint feature map;
the transfer module is used for calculating transfer matrixes between the fingerprint feature image to be authenticated and the feature matrixes of each group of corresponding channel dimensions of the input fingerprint feature image so as to obtain a transfer feature image composed of a plurality of transfer matrixes;
the optimizing module is used for optimizing the transfer characteristic diagram to obtain an optimized transfer characteristic diagram; and
the classification module is used for enabling the optimized transfer feature map to pass through a classifier to obtain a classification result, wherein the classification result is used for indicating whether the fingerprint image to be authenticated belongs to a first identity tag or not;
wherein, the optimization module includes:
calculating quantized transferable sensing factors of each feature matrix of the transfer feature map relative to transferable features of the transfer feature map to obtain a plurality of transferable sensing factors; and
weighting each feature matrix of the transfer feature map with the plurality of transferable sensing factors to obtain the optimized transfer feature map;
Wherein calculating quantized transferable sensing factors of each feature matrix of the transfer feature map relative to transferable features of the transfer feature map to obtain a plurality of transferable sensing factors comprises:
calculating quantized transferable sensing factors of each feature matrix of the transfer feature map relative to transferable features of the transfer feature map with the following factor calculation formula to obtain a plurality of transferable sensing factors;
wherein,the factor calculation formula is as follows:wherein,is the first of the transfer characteristic diagramsThe number of feature matrices is chosen such that,each of which isThe first feature matrixThe value of the characteristic is a value of,is a graph of the transfer characteristics,is the first of the transfer characteristic diagramsThe value of the characteristic is a value of,is a logarithm based on 2, andis a weighted super-parameter that is used to determine the weight of the object,is the firstA transferable sensing factor.
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