CN113610632B - Bank outlet face recognition method and device based on blockchain - Google Patents
Bank outlet face recognition method and device based on blockchain Download PDFInfo
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Abstract
The invention discloses a bank website face recognition method and device based on a blockchain, and relates to the technical field of blockchains, wherein the method comprises the following steps: acquiring historical transaction data of each banking website; determining transaction risk coefficients of all banking sites according to historical transaction data of all banking sites; estimating face recognition parameter thresholds of all banking sites according to transaction risk coefficients of all banking sites by utilizing intelligent contracts which are pre-configured on a blockchain network, wherein the face recognition parameter threshold of each banking site is a parameter threshold used when each banking site carries out face recognition on a client; and carrying out face recognition on the clients in the edge computing systems of the banking sites according to the face recognition parameter threshold values of the banking sites, and generating face recognition results. The invention can effectively reduce waiting time of customer face recognition and lighten the pressure of a bank face recognition system.
Description
Technical Field
The invention relates to the technical field of blockchains, in particular to a banking point face recognition method and device based on a blockchain.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
Currently, customer face recognition of banking outlets is usually completed in a background server of a bank. This approach not only results in a longer time for face recognition, but centralized face recognition can put a greater strain on the backend servers. When the network of banking outlets is not good, face recognition takes more time, resulting in poor customer experience.
With the advancement of edge computing technology, face recognition at banking sites or at self-service equipment of banks is made possible. However, due to uncertainty of factors such as places, the risk of face recognition at banking sites is much higher than the risk of face recognition at a back-end server of a bank, and if the configured face parameters are unreasonable, various transaction risks can be caused.
Disclosure of Invention
The embodiment of the invention provides a bank website face recognition method based on a blockchain, which is used for solving the technical problems that in the prior art, the face recognition process consumes longer time and the system pressure of a background server is higher when a bank background server carries out the face recognition of a client, and comprises the following steps: acquiring historical transaction data of each banking website; determining transaction risk coefficients of all banking sites according to historical transaction data of all banking sites; estimating face recognition parameter thresholds of all banking sites according to transaction risk coefficients of all banking sites by utilizing intelligent contracts which are pre-configured on a blockchain network, wherein the face recognition parameter threshold of each banking site is a parameter threshold used when each banking site carries out face recognition on a client; and carrying out face recognition on the clients in the edge computing systems of the banking sites according to the face recognition parameter threshold values of the banking sites, and generating face recognition results.
The embodiment of the invention also provides a bank website face recognition device based on the blockchain, which is used for solving the technical problems that the face recognition process is long in time consumption and the system pressure of a background server is high in the prior art when the face recognition of a client is carried out on the background server of a bank, and comprises the following steps: the transaction data acquisition module is used for acquiring historical transaction data of each banking website; the transaction risk coefficient determining module is used for determining transaction risk coefficients of all banking sites according to historical transaction data of all banking sites; the face recognition parameter threshold estimation module is used for estimating face recognition parameter thresholds of all banking sites according to transaction risk coefficients of all banking sites by utilizing intelligent contracts which are pre-configured on a blockchain network, wherein the face recognition parameter threshold of each banking site is a parameter threshold used when each banking site carries out face recognition on a client; and the face recognition module is used for recognizing the face of the client in the edge computing system of each bank website according to the face recognition parameter threshold value of each bank website, and generating a face recognition result.
The embodiment of the invention also provides computer equipment which is used for solving the technical problems that the human face recognition of a client is carried out on a background server of a bank in the prior art, the human face recognition process consumes longer time and the system pressure of the background server is higher.
The embodiment of the invention also provides a computer readable storage medium for solving the technical problems that the human face recognition process is long in time consumption and the system pressure of a background server is high in the prior art when the human face recognition of a client is carried out on the background server of a bank, and the computer readable storage medium is stored with a computer program for executing the bank website human face recognition method based on the blockchain.
According to the bank point face recognition method, device, computer equipment and computer readable storage medium based on the blockchain, after the historical transaction data of each bank point are obtained, the transaction risk coefficient of each bank point is determined according to the historical transaction data of each bank point, then the face recognition parameter threshold value used when each bank point performs face recognition on a client is estimated according to the transaction risk coefficient of each bank point by utilizing the intelligent contract pre-configured on the blockchain network, and finally face recognition is performed on the client in the edge computing system of each bank point according to the face recognition parameter threshold value of each bank point, so that a face recognition result is generated.
Compared with the technical scheme that the face recognition of the client is carried out on the background server of the bank in the prior art, the embodiment of the invention carries out the face recognition on the client in the edge computing system of the bank, can effectively reduce the waiting time of the face recognition of the client, lightens the pressure of the bank face recognition system, utilizes the intelligent contracts which are pre-configured on the blockchain network to estimate the face recognition parameter threshold value used when each bank carries out the face recognition on the client, and can reduce the transaction risk existing when the bank edge computing system is utilized to carry out the face recognition on the client.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a flowchart of a banking point face recognition method based on blockchain in an embodiment of the present invention;
fig. 2 is a block chain-based face recognition flowchart provided in an embodiment of the present invention;
FIG. 3 is a block chain-based cross-site face recognition flow chart provided in an embodiment of the invention;
Fig. 4 is a flowchart for acquiring a face recognition parameter threshold according to an embodiment of the present invention;
Fig. 5 is a flowchart of adjusting a face recognition parameter threshold according to an embodiment of the present invention;
Fig. 6 is a schematic diagram of a banking point face recognition device based on a blockchain in an embodiment of the present invention;
Fig. 7 is a schematic diagram of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
The embodiment of the invention provides a banking point face recognition method based on a blockchain, and fig. 1 is a flowchart of the banking point face recognition method based on the blockchain, as shown in fig. 1, and the method comprises the following steps:
S101, historical transaction data of each banking website is obtained.
It should be noted that, in the embodiment of the present invention, the historical transaction data obtained through the above S101 includes, but is not limited to: risk transaction data and customer transaction data. The customer transaction data may include, among other things, customer work addresses, home addresses, etc. that are able to predict a customer set for each banking site. From the historical transaction data for each banking endpoint, a customer set for each banking endpoint may be determined.
In one embodiment, each bank node may collect its transaction data in real time and upload the collected transaction data to a blockchain network, which may be a blockchain network formed by each bank node as a blockchain storage node, or may be a separate blockchain network, where each bank node communicates with the blockchain network through a blockchain client, receives data pushed by the blockchain network, or uploads data to the blockchain network.
S102, determining transaction risk coefficients of all banking sites according to historical transaction data of all banking sites.
It should be noted that the transaction risk coefficient is used to measure a quantization index of possible loss to the customer when the transaction is performed at each banking website. Because different banking sites are in different environments and the customer base of service is also different, the transaction risk coefficients for different banking sites are different. After the historical transaction data of each banking website are obtained, the total transaction data of each banking website, the total transaction data related to face recognition, the risk transaction data and the risk transaction data related to face recognition can be counted, so that the transaction risk coefficient of each banking website is determined.
In one embodiment, the transaction risk coefficients for each banking outlet may be calculated by: determining risk types according to risk transaction data (or risk transaction data related to face recognition) in historical transaction data of each banking website, wherein the risk data accounts for the whole transaction data, and risk indexes of each risk type, wherein the risk indexes comprise risk coefficients and probabilities; and determining the transaction risk coefficient of each banking website according to the proportion of the risk data to the whole transaction data and the risk index of each risk type. The impact of different types of risk on the customer is different. The method can determine the loss of the corresponding risk type to the client based on the data corresponding to each risk type, a large amount of data is arranged in a bank database, the law of the loss of the risk to the client is known by a large number theorem, the risk value corresponding to each risk type can be measured by using a statistic average value, namely the risk coefficient corresponding to each risk type is set as the average value of the loss of the corresponding risk type to the client. Meanwhile, the probability of each risk type also reflects the influence of the risk type on the client, and the larger the probability is, the larger the influence is, and the larger the risk of the client is. The proportion of risk data to the overall transaction data also reflects the impact of risk on the transaction. After determining the risk types, the risk coefficient of each risk type, the probability of each risk type, and the proportion of the risk data to the whole transaction data, the transaction risk coefficient of the corresponding banking website can be determined, for example, the method is directly set as follows: t×sum (log (ri×ni)) or t×sum (ri×ni), where ri is a risk coefficient of the i-th risk type, ni is a probability of the i-th risk type, t is a proportion of risk data to the entire transaction data, and sum is a summation function.
S103, estimating face recognition parameter thresholds of all the banking sites according to transaction risk coefficients of all the banking sites by utilizing intelligent contracts which are pre-configured on a blockchain network, wherein the face recognition parameter threshold of each banking site is a parameter threshold used when each banking site carries out face recognition on a client.
It should be noted that, the intelligent contract in the embodiment of the present invention refers to a section of program preconfigured on the blockchain network, and the face recognition parameter threshold used when the face recognition is performed on the client is estimated according to the transaction risk coefficient of each banking website by using the intelligent contract preconfigured on the blockchain network.
S104, carrying out face recognition on the clients in the edge computing system of each bank website according to the face recognition parameter threshold value of each bank website, and generating a face recognition result.
In the implementation, after the face recognition parameter threshold value of each bank website is determined, the face recognition parameter threshold value of each bank website can be stored in the edge computing system of each bank website, so that each bank website can directly perform face recognition on the client in the edge computing system.
In order to reduce transaction risk existing in the face recognition of the website, in one embodiment, as shown in fig. 2, in the bank website face recognition method based on the blockchain provided in the embodiment of the present invention, S104 may generate the face recognition result by the following steps:
s201, storing face recognition parameter thresholds of all banking sites on a blockchain network;
s202, carrying out face recognition on clients in an edge computing system of each bank website according to face recognition parameter thresholds of each bank website stored on the blockchain network, and generating face recognition results.
In the embodiment of the invention, the face recognition parameter threshold values of all banking sites are stored on the blockchain network, so that the face recognition parameter threshold values can be ensured to be untampered.
In the method for recognizing the bank website face based on the blockchain provided by the embodiment of the invention, as shown in fig. 3, the step S104 can be realized by the following steps when being implemented:
S301, judging whether a target client is in a client set of target banking outlets or not when a business transaction request initiated by the target client to the target banking outlets is received, wherein the target client is any client, and the target banking outlets are any banking outlets;
S302, if the target client is not in the client set of the target banking website, carrying out face recognition on the target client at a banking background server according to a face recognition parameter threshold stored in advance at the banking background server to generate a face recognition result;
s303, if the target client is in the client set of the target banking website, performing face recognition on the target client in the edge computing system of the target banking website according to the face recognition parameter threshold of the target banking website, and generating a face recognition result.
The face recognition parameter threshold value and the probability of the occurrence of transaction risk of face recognition are in an inverse relation, namely, the higher the face recognition parameter threshold value is set, the lower the risk occurrence probability is. The greater the risk coefficient of a bank website, the greater the probability of occurrence risk of the face recognition under the premise of the same face recognition parameter threshold value, and in order to ensure that the probability of occurrence risk of the face recognition of the bank website is lower, the face recognition parameter threshold value corresponding to the bank website needs to be properly increased.
To ensure that the risk of occurrence of face recognition at banking sites is low, in one embodiment, the face recognition parameter threshold of each banking site is estimated by equation (1) according to the transaction risk coefficient of each banking site using a pre-configured intelligent contract on the blockchain network:
L2＝MAXL-(MAXL-L1)×f(r) (1)
wherein, L 2 represents a face recognition parameter threshold value when each bank website carries out face recognition on the client; MAXL denotes the maximum value of the face recognition parameter threshold; l1 represents a face recognition parameter threshold value when a bank background server carries out face recognition on a client; f represents a monotonically decreasing function; r represents a transaction risk factor. The formula can ensure that the face recognition parameter threshold value of the banking website is directly related to the transaction risk of the banking website, and the larger the transaction risk coefficient is, the larger the face recognition parameter threshold value of the banking website is.
In another embodiment, the parameter threshold for face recognition is directly associated with the pass rate or misrecognition of face recognition. As shown in fig. 4, in the blockchain-based banking point face recognition method provided by the embodiment of the present invention, the face recognition parameter threshold of each banking point may be obtained by the following steps:
s401, calculating the passing rate or the false recognition rate of each bank website according to the transaction risk coefficient of each bank website by utilizing an intelligent contract pre-configured on a blockchain network;
s402, acquiring face recognition parameter thresholds of all the banking sites according to the passing rate or the false recognition rate of all the banking sites.
In the embodiment of the present invention, the passing rate or the recognition rate and the face recognition parameter threshold should be in a one-to-one correspondence, and the larger the face recognition parameter threshold is, the lower the passing rate is, and the higher the recognition rate is. s
Further, in the blockchain-based bank point face recognition method provided by the embodiment of the invention, the passing rate or the false recognition rate of each bank point can be calculated through the following formula:
T2＝1-(1-T1)/f(r) (2)
F2＝1-(1-F1)×f(r) (3)
Wherein T 2 represents the passing rate of each bank node when each bank node performs face recognition on the client; t 1 represents the passing rate of each banking website when the banking background server carries out face recognition on the client; f 2 represents the false recognition rate of each bank website when each bank website carries out face recognition on the client; f 1 represents the false recognition rate of each bank website when the bank background server carries out face recognition on the client; f represents a monotonically decreasing function; r represents a transaction risk factor. The formula can also ensure that the face recognition parameter threshold value of the banking website is directly related to the transaction risk of the banking website, and the larger the transaction risk coefficient is, the larger the face recognition parameter threshold value of the banking website is.
Alternatively, the monotonically decreasing function employed in the embodiment of the present invention may be as shown in formula (4):
f(r)＝1-r (4)
where f (r) represents a monotonically decreasing function with r as an argument.
The parameter setting of the face recognition of the bank server is mature, a large amount of data is available, and the risk is basically controllable. The risk transaction proportion corresponding to the face recognition of the bank server can be set as a reference risk transaction proportion, and then the face recognition parameter threshold value of each bank website is adjusted according to the reference risk transaction proportion.
In one embodiment, as shown in fig. 5, the blockchain-based banking point face recognition method provided in the embodiment of the present invention may further adjust the face recognition parameter threshold of each banking point by:
s501, acquiring transaction data of each banking website for carrying out face recognition on clients;
S502, determining the risk transaction proportion of each banking website according to transaction data of each banking website for carrying out face recognition on clients;
S503, judging whether the risk transaction proportion of each banking website is larger than a reference risk transaction proportion, wherein the reference risk transaction proportion is a risk transaction proportion corresponding to face recognition of a banking server;
And S504, when the risk transaction proportion of any bank website is larger than the reference risk transaction proportion, increasing the face recognition parameter threshold value of the bank website until the risk transaction proportion of the bank website is smaller than or equal to the reference risk transaction proportion.
Specifically, the face recognition parameter threshold value of the bank website or the monotonically decreasing function of the passing rate or the false recognition rate of the face recognition can be adjusted and determined until the face recognition parameter threshold value of the bank website, so that the risk transaction proportion of the bank website is smaller than or equal to the reference risk transaction proportion.
As an optional solution, according to the blockchain-based banking point face recognition method provided in the embodiment of the present invention, a method for setting a banking transaction face parameter applicable to different transaction risks of a banking point can be provided, which specifically includes:
1) And predicting the client set of the banking website according to the historical transaction data (such as the working address, the family address and the like of the client), namely determining the client set corresponding to the banking website. And acquiring face recognition data of the customer set from a bank face recognition system, downloading the data into a database of the bank website, and encrypting by using a public key of the bank website before downloading the data. The data should also include parameters for face recognition at the bank server.
2) And acquiring the proportion r of risk data in the historical transaction data of the banking website. Such as the ratio of the sum of risk data relating to face recognition in risk transactions to the sum of data relating to face recognition in total transactions, as a transaction risk factor.
3) And estimating parameters of face recognition by clients by using intelligent contracts of the blockchain, for example, setting a threshold value of face recognition of a high-risk scene to L 2 according to the transaction risk coefficient. L1 is a face recognition threshold for face recognition at a bank server, MAXL is the maximum value of the face recognition threshold. L1 and L 2 correspond to the same customer. And storing the calculated parameters into a blockchain network.
4) The new threshold is estimated using the pass rate and the false recognition rate. For example, the pass rate of face recognition in the bank website edge computing system is set to be T 2,T1, which is the pass rate of face recognition when the bank server performs face recognition, and the threshold value for performing the face recognition algorithm in the bank website edge computing system is found based on the corresponding relationship between the pass rate of the face recognition algorithm and the threshold value. The false recognition rate is calculated similarly, and the false recognition rate of the face recognition in the bank website edge computing system is set to be F 2,F1 to be the false recognition rate of the face recognition when the bank server performs the face recognition.
5) When the client makes a transaction at a banking site, whether the client is in a client set corresponding to the banking site is confirmed. If yes, carrying out face recognition in an edge computing system of the bank website based on the parameters determined in the steps; if not, the method returns to the face recognition system of the bank.
6) And acquiring transaction data for face recognition in an edge computing system of a banking website, and confirming the proportion p of risk data of the data. If the proportion p is larger than the corresponding proportion q of the risk transaction of the bank face recognition system, the function f of the step 3 is adjusted to enable L2 to be larger until the corresponding proportion is smaller than or equal to q.
Based on the same inventive concept, the embodiment of the invention also provides a bank website face recognition device based on the blockchain, as described in the following embodiment. Because the principle of the device for solving the problem is similar to that of the bank point face recognition method based on the blockchain, the implementation of the device can be referred to the implementation of the bank point face recognition method based on the blockchain, and the repetition is omitted.
Fig. 6 is a schematic diagram of a banking point face recognition device based on blockchain according to an embodiment of the present invention, as shown in fig. 6, the device includes: a transaction data acquisition module 61, a transaction risk factor determination module 62, a face recognition parameter threshold estimation module 63 and a face recognition module 64.
The transaction data acquisition module 61 is configured to acquire historical transaction data of each banking website;
the transaction risk factor determining module 62 is configured to determine a transaction risk factor of each banking website according to historical transaction data of each banking website;
The face recognition parameter threshold estimation module 63 is configured to estimate face recognition parameter thresholds of each bank node according to transaction risk coefficients of each bank node by using intelligent contracts pre-configured on the blockchain network, where the face recognition parameter threshold of each bank node is a parameter threshold used when each bank node performs face recognition on a client;
The face recognition module 64 is configured to perform face recognition on the client in the edge computing system of each bank node according to the face recognition parameter threshold of each bank node, and generate a face recognition result.
In one embodiment, as shown in fig. 6, the banking point face recognition device based on blockchain provided in the embodiment of the present invention may further include: the blockchain data storage module 65 is used for storing face recognition parameter thresholds of all banking sites on a blockchain network; in this embodiment, the face recognition module 64 is further configured to perform face recognition on the client in the edge computing system of each bank node according to the face recognition parameter threshold of each bank node stored on the blockchain network, so as to generate a face recognition result.
In one embodiment, in the banking point face recognition device based on blockchain provided in the embodiment of the present invention, the face recognition module 64 is further configured to: when a business transaction request initiated by a target client to a first banking website is received, judging whether the target client is in a first client set of the first banking website, wherein the first banking website is any banking website; inquiring face recognition parameter threshold values of second banking sites from the blockchain network under the condition that the target client is not in the first client set, wherein the target client is a client in the second client set, and the second client set is a client set of the second banking sites; and carrying out face recognition on the target client according to the face recognition parameter threshold value of the second bank website, and generating a face recognition result.
In one embodiment, as shown in fig. 6, the banking outlet face recognition device based on blockchain provided in the embodiment of the present invention further includes: the face recognition parameter threshold value obtaining module 66 is configured to calculate the passing rate or the false recognition rate of each bank website according to the transaction risk coefficient of each bank website by using an intelligent contract pre-configured on the blockchain network; and acquiring face recognition parameter thresholds of the banking sites according to the passing rate or the false recognition rate of the banking sites.
In one embodiment, in the blockchain-based banking point face recognition device provided in the embodiment of the present invention, the face recognition parameter threshold estimation module 63 is further configured to estimate the face recognition parameter threshold of each banking point through the above formula (1).
In one embodiment, in the blockchain-based bank point face recognition device provided in the embodiment of the present invention, the face recognition parameter threshold estimation module 63 is further configured to calculate the passing rate of each bank point according to the above formula (2), and calculate the false recognition rate of each bank point according to the above formula (3).
In one embodiment, in the blockchain-based banking point face recognition device provided in the embodiment of the present invention, the face recognition parameter threshold estimation module 63 is further configured to: acquiring transaction data of each banking website for carrying out face recognition on clients; determining the risk transaction proportion of each banking website according to transaction data of each banking website for carrying out face recognition on clients; judging whether the risk transaction proportion of each banking website is larger than a reference risk transaction proportion, wherein the reference risk transaction proportion is a risk transaction proportion corresponding to face recognition of a banking server; and when the risk transaction proportion of any bank website is larger than the reference risk transaction proportion, increasing the face recognition parameter threshold value of the bank website until the risk transaction proportion of the bank website is smaller than or equal to the reference risk transaction proportion.
Based on the same inventive concept, the embodiment of the present invention further provides a computer device, which is used for solving the technical problems of long time consumption in the face recognition process and large system pressure of the background server in the face recognition process of the customer performed on the background server of the bank in the prior art, and fig. 7 is a schematic diagram of the computer device provided in the embodiment of the present invention, as shown in fig. 7, the computer device 70 includes a memory 701, a processor 702 and a computer program stored on the memory 701 and capable of running on the processor 702, and the processor 702 implements the bank website face recognition method based on the blockchain when executing the computer program.
Based on the same inventive concept, the embodiment of the invention also provides a computer readable storage medium, which is used for solving the technical problems that the human face recognition process of a client is long in time consumption and the system pressure of a background server is high in the prior art when the human face recognition of the client is carried out on the background server of a bank, and the computer readable storage medium is stored with a computer program for executing the bank website human face recognition method based on the blockchain.
In summary, the blockchain-based method, device, computer equipment and computer readable storage medium for identifying the bank website provided by the embodiment of the invention determine the transaction risk coefficient of each bank website according to the historical transaction data of each bank website after the historical transaction data of each bank website is obtained, further estimate the face identification parameter threshold value used when each bank website identifies the customer according to the transaction risk coefficient of each bank website by using the intelligent contract pre-configured on the blockchain network, and finally identify the face of the customer in the edge computing system of each bank website according to the face identification parameter threshold value of each bank website to generate the face identification result.
Compared with the technical scheme that the face recognition of the client is carried out on the background server of the bank in the prior art, the embodiment of the invention carries out the face recognition on the client in the edge computing system of the bank, can effectively reduce the waiting time of the face recognition of the client, lightens the pressure of the bank face recognition system, utilizes the intelligent contracts which are pre-configured on the blockchain network to estimate the face recognition parameter threshold value used when each bank carries out the face recognition on the client, and can reduce the transaction risk existing when the bank edge computing system is utilized to carry out the face recognition on the client.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, 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 specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. The bank outlet face recognition method based on the blockchain is characterized by comprising the following steps of:
Acquiring historical transaction data of each banking website;
determining transaction risk coefficients of all banking sites according to historical transaction data of all banking sites;
Estimating face recognition parameter thresholds of all banking sites according to transaction risk coefficients of all banking sites by utilizing intelligent contracts which are pre-configured on a blockchain network, wherein the face recognition parameter threshold of each banking site is a parameter threshold used when each banking site carries out face recognition on a client;
According to the face recognition parameter threshold value of each bank website, carrying out face recognition on the client in the edge computing system of each bank website to generate a face recognition result;
According to the face recognition parameter threshold value of each bank website, carrying out face recognition on the client in the edge computing system of each bank website to generate a face recognition result, wherein the face recognition result comprises the following steps:
Storing face recognition parameter thresholds of all banking sites on a blockchain network;
According to the face recognition parameter threshold value of each bank website stored on the blockchain network, carrying out face recognition on the client in the edge computing system of each bank website to generate a face recognition result;
According to the face recognition parameter threshold value of each bank website, carrying out face recognition on the client in the edge computing system of each bank website to generate a face recognition result, wherein the face recognition result comprises the following steps:
When a business transaction request initiated by a target client to a target banking website is received, judging whether the target client is in a client set of the target banking website, wherein the target client is any client, and the target banking website is any banking website;
if the target client is not in the client set of the target banking website, carrying out face recognition on the target client at a banking background server according to a face recognition parameter threshold stored in advance at the banking background server to generate a face recognition result;
If the target client is in the client set of the target banking website, performing face recognition on the target client in an edge computing system of the target banking website according to a face recognition parameter threshold of the target banking website to generate a face recognition result;
Estimating face recognition parameter thresholds of each bank website according to transaction risk coefficients of each bank website by utilizing intelligent contracts pre-configured on a blockchain network, wherein the face recognition parameter thresholds comprise:
Calculating the passing rate or the false recognition rate of each bank website according to the transaction risk coefficient of each bank website by utilizing an intelligent contract pre-configured on the blockchain network;
acquiring face recognition parameter thresholds of all banking sites according to the passing rate or the false recognition rate of all banking sites;
the passing rate or the false recognition rate of each bank website is calculated by the following formula:
T2＝1-(1-T1)/f(r)；
F2＝1-(1-F1)×f(r)；
Wherein T 2 represents the passing rate of each bank node when each bank node performs face recognition on the client; t 1 represents the passing rate of each banking website when the banking background server carries out face recognition on the client; f 2 represents the false recognition rate of each bank website when each bank website carries out face recognition on the client; f 1 represents the false recognition rate of each bank website when the bank background server carries out face recognition on the client; f (r) represents a monotonically decreasing function; r represents a transaction risk factor.
2. The method of claim 1, wherein the face recognition parameter threshold for each banking point is estimated by the formula:
L2＝MAXL-(MAXL-L1)×f(r)；
Wherein, L 2 represents a face recognition parameter threshold value when each bank website carries out face recognition on the client; MAXL denotes the maximum value of the face recognition parameter threshold; l1 represents a face recognition parameter threshold value when a bank background server carries out face recognition on a client; f (r) represents a monotonically decreasing function; r represents a transaction risk factor.
3. The method of claim 1, wherein the method further comprises:
acquiring transaction data of each banking website for carrying out face recognition on clients;
Determining the risk transaction proportion of each banking website according to transaction data of each banking website for carrying out face recognition on clients;
Judging whether the risk transaction proportion of each banking website is larger than a reference risk transaction proportion, wherein the reference risk transaction proportion is a risk transaction proportion corresponding to face recognition of a banking server;
And when the risk transaction proportion of any bank website is larger than the reference risk transaction proportion, increasing the face recognition parameter threshold value of the bank website until the risk transaction proportion of the bank website is smaller than or equal to the reference risk transaction proportion.
4. A blockchain-based bank point face recognition device, comprising:
the transaction data acquisition module is used for acquiring historical transaction data of each banking website;
the transaction risk coefficient determining module is used for determining transaction risk coefficients of all banking sites according to historical transaction data of all banking sites;
The face recognition parameter threshold estimation module is used for estimating face recognition parameter thresholds of all banking sites according to transaction risk coefficients of all banking sites by utilizing intelligent contracts which are pre-configured on a blockchain network, wherein the face recognition parameter threshold of each banking site is a parameter threshold used when each banking site carries out face recognition on a client;
the face recognition module is used for recognizing the face of the client in the edge computing system of each bank website according to the face recognition parameter threshold value of each bank website to generate a face recognition result;
The apparatus further comprises:
the block chain data storage module is used for storing face recognition parameter thresholds of all banking sites on a block chain network;
the face recognition module is further used for carrying out face recognition on clients in the edge computing system of each bank node according to the face recognition parameter threshold value of each bank node stored in the blockchain network to generate a face recognition result;
The face recognition module is also used for:
When a business transaction request initiated by a target client to a target banking website is received, judging whether the target client is in a client set of the target banking website, wherein the target banking website is any banking website;
if the target client is not in the client set of the target banking website, carrying out face recognition on the target client at a banking background server according to a face recognition parameter threshold stored in advance at the banking background server to generate a face recognition result;
If the target client is in the client set of the target banking website, performing face recognition on the target client in an edge computing system of the target banking website according to a face recognition parameter threshold of the target banking website to generate a face recognition result;
The apparatus further comprises:
the face recognition parameter threshold value acquisition module is used for calculating the passing rate or the false recognition rate of each bank website according to the transaction risk coefficient of each bank website by utilizing an intelligent contract pre-configured on the blockchain network; acquiring face recognition parameter thresholds of all the banking sites according to the passing rate or the false recognition rate of all the banking sites;
The face recognition parameter threshold estimation module is also used for calculating the passing rate or the false recognition rate of each bank website through the following formula:
T2＝1-(1-T1)/f(r)；
F2＝1-(1-F1)×f(r)；
wherein T 2 represents the passing rate of each bank node when each bank node performs face recognition on the client; t1 represents the passing rate of each banking website when the banking background server carries out face recognition on the client; f 2 represents the false recognition rate of each bank website when each bank website carries out face recognition on the client; f1 represents the false recognition rate of each bank website when the bank background server carries out face recognition on the client; f (r) represents a monotonically decreasing function; r represents a transaction risk factor.
5. The apparatus of claim 4, wherein the face recognition parameter threshold estimation module is further configured to estimate the face recognition parameter threshold for each banking point by:
L2＝MAXL-(MAXL-L1)×f(r)；
Wherein, L 2 represents a face recognition parameter threshold value when each bank website carries out face recognition on the client; MAXL denotes the maximum value of the face recognition parameter threshold; l1 represents a face recognition parameter threshold value when a bank background server carries out face recognition on a client; f (r) represents a monotonically decreasing function; r represents a transaction risk factor.
6. The apparatus of claim 4, wherein the face recognition parameter threshold estimation module is further to:
acquiring transaction data of each banking website for carrying out face recognition on clients;
Determining the risk transaction proportion of each banking website according to transaction data of each banking website for carrying out face recognition on clients;
Judging whether the risk transaction proportion of each banking website is larger than a reference risk transaction proportion, wherein the reference risk transaction proportion is a risk transaction proportion corresponding to face recognition of a banking server;
And when the risk transaction proportion of any bank website is larger than the reference risk transaction proportion, increasing the face recognition parameter threshold value of the bank website until the risk transaction proportion of the bank website is smaller than or equal to the reference risk transaction proportion.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the blockchain-based banking point face identification method of any of claims 1 to 3 when the computer program is executed.
8. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the method of any of claims 1 to 3.
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