CN110348840B - Small-amount secret-free payment system improved by using biometric identification technology - Google Patents

Small-amount secret-free payment system improved by using biometric identification technology Download PDF

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CN110348840B
CN110348840B CN201910467491.3A CN201910467491A CN110348840B CN 110348840 B CN110348840 B CN 110348840B CN 201910467491 A CN201910467491 A CN 201910467491A CN 110348840 B CN110348840 B CN 110348840B
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CN110348840A (en
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孔路恒
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Beijing Yuda Tianli Technology Development Co ltd
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    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/22—Payment schemes or models
    • G06Q20/29—Payment schemes or models characterised by micropayments
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4014—Identity check for transactions
    • G06Q20/40145—Biometric identity checks

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Abstract

The invention relates to a small-amount secret-free payment system improved by using a biological identification technology. The invention adopts gradient operators to respectively calculate the gradient amplitude of each region and carry out normalization processing, respectively sets the weights of the four regions, calculates the quantized average value XP of the boundary of the four small regions, and calculates the quantized values X of the four main characteristics by the same method when the precision is satisfiedZLWhen the accuracy is met, calculating the quality evaluation R, meeting the accuracy judgment, judging the quality evaluation meeting the accuracy, and improving the accuracy of quality evaluation judgment, wherein the average value XP and the value X are quantizedZAnd when any one of the quality evaluation R does not meet the precision, immediately enabling the microprocessor to re-acquire the biological identification information, and improving the efficiency of quality evaluation judgment.

Description

Small-amount secret-free payment system improved by using biometric identification technology
Technical Field
The invention relates to the technical field of biometric payment, in particular to a small-amount secret-free payment system improved by using a biometric technology.
Background
With the development of the intelligent era, online payment is used, mobile phone payment modes are more and more common, a user needs to input a payment password of 6 digits or show a two-dimensional code to complete a payment process, the user uses a mobile phone to pay in public places, the password is easy to be stolen or the two-dimensional code is easy to be stolen by pedestrians and the like, and potential safety hazards exist.
In order to simplify user operation and improve payment safety, a payment mode based on a biological identification technology is provided, such as fingerprint identification, face identification, iris identification, vein identification and the like, biological identification information is collected through a biological identification information collection module and is transmitted to a microprocessor, the microprocessor is transmitted to a financial service transaction center through a transmission module, the financial service transaction matches the biological identification information with biological identification information prestored in a database, the matching is successful, namely, the matching result is verified, and is transmitted to the microprocessor through the transmission module, the microprocessor controls a payment terminal to be allowed to conduct secret-free transaction, if the quality of the collected biological identification information has serious defects, when the biological identification information is matched with the biological identification information prestored in the database, the requirement of the matching cannot be met, the biological identification information is fed back to the microprocessor to remind of being collected again, and the subsequent efficiency of matching with the biological identification information in the database is directly influenced, Accuracy, therefore, the quality of the collected biometric information needs to be evaluated, and the biometric information meeting the quality is transmitted to the financial service transaction center through the transmission module.
The present invention therefore provides a new solution to this problem.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a small-amount secret-free payment system improved by using a biological identification technology, which can evaluate the quality of the collected biological identification information and transmit the biological identification information meeting the quality to a financial service transaction center through a transmission module.
In order to achieve the purpose, the invention is realized by the following technical scheme: including biological identification information acquisition module, microprocessor, transmission module, financial service transaction center, payment end, a serial communication port, the biological identification information that biological identification information acquisition module gathered is transmitted to microprocessor after the characteristic is drawed, microprocessor assesses the quality analysis to biological identification information, when satisfying the quality precision, convey to financial service transaction center through transmission module, the biological identification information that prestores in financial service transaction center and the database matches, output matching result sends microprocessor to through transmission module, microprocessor control whether allows payment end to carry out the transaction of exempting from the secret:
the specific method for evaluating quality analysis of biological identification information comprises the following steps:
s1, obtaining the biological identification information after feature extraction, and further dividing into four small areas X1，X2，X3，X4The four small regions form a matrix form, and eight edges are formed between the adjacent small regions in the row and column directions;
s2, extracting eight edges of the four small regions and the boundaries of the main features by adopting an edge detection method;
s3, calculating step 2 extraction by gradient operatorRespectively calculating a quantized average value XP and a quantized value XZL；
S4, calculating the quality assessment R, wherein the formula is R XP × 40% + XZL×60％；
S5, comparing the quality evaluation R with a threshold value of 0.8, binarizing, and converting into 1 or 0 to indicate that the precision is met or not met;
and S6, when the biological identification information is satisfied, the microprocessor controls the biological identification information to be transmitted to the financial service transaction center through the transmission module, and when the biological identification information is not satisfied, the microprocessor acquires the biological identification information again.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages:
1, dividing the collected biological identification information into four small areas, and extracting eight edges of the four small areas and boundaries of main features by using an edge detection method for improving the accuracy of quality evaluation;
2, respectively calculating the gradient amplitude X of each region by adopting a gradient operator1A，A2A，A3A，A4ANormalizing to make the gradient amplitude between 0-1, setting the weights of the four regions as 28%, 35%, 22% and 15%, calculating the quantized average XP of the boundaries of the four small regions, comparing with the threshold of 0.3, and calculating the quantized values X of the four main features by the same method when the precision is satisfiedZLComparing with the threshold value of 0.5, calculating the quality evaluation R when the accuracy is satisfied, comparing with the threshold value of 0.8, and controlling by the microprocessor to transmit to the financial service transaction center via the transmission module when the accuracy is satisfied, thereby improving the accuracy of quality evaluation judgment, wherein the average value XP and the value X are quantizedZAnd when any one of the quality evaluation R does not meet the precision, immediately enabling the microprocessor to re-acquire the biological identification information, and improving the efficiency of quality evaluation judgment.
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FIG. 1 is an overall block diagram of the present invention.
FIG. 2 is a flow chart of the overall steps of the present invention.
FIG. 3 is a diagram illustrating the calculation of the mean XP and X values of the present inventionZLIs shown in the figure.
Detailed Description
The foregoing and other technical matters, features and effects of the present invention will be apparent from the following detailed description of the embodiments, which is to be read in connection with the accompanying drawings of fig. 1 to 3. The structural contents mentioned in the following embodiments are all referred to the attached drawings of the specification.
In order to verify the feasibility and the practical use effect of the method, the method is analyzed and verified below by way of example.
The embodiment I is a small-amount secret-free payment system improved by using a biological identification technology, biological identification information (fingerprint identification, face identification and iris identification, and specific identification can be carried out through a corresponding identification module) acquired by a biological identification information acquisition module is transmitted to a microprocessor (which can be a single chip microcomputer) after feature extraction, the microprocessor evaluates quality analysis is carried out on the biological identification information, when quality precision is met, the biological identification information is transmitted to a financial service transaction center (such as a mobile financial service transaction center) through a transmission module (which can be a GPRS module and a 3G/4G communication network), the financial service transaction center is matched with the biological identification information in a data prestoring library, a matching result is output and transmitted to the microprocessor through the transmission module, and the microprocessor controls whether a payment end is allowed to carry out secret-free transaction:
the specific method for evaluating quality analysis of biological identification information comprises the following steps:
s1, obtaining the biological identification information after feature extraction, classifying the biological identification information according to the feature variables (the biological identification information: the structural types-roundness of fingerprints, human faces and irises, such as the fingerprints are arch-shaped, tent-arch-shaped, left-side-shaped, right-side-skip-shaped and spiral-shaped, the human faces are standard, O-shaped, square, triangular, diamond-shaped, nail-shaped and long, the irises are almond-shaped, danfeng-shaped, deep-cave eyes, thick-convex eyes, lower-hanging eyes and upper-oblique eyes, and the sizes, such as standard, large and small, of the fingerprints, the human faces and the irises), further dividing the biological identification information into four small regions X1, X2, X3 and X4 (the biological identification information can be divided according to the main biological characteristics of the biological identification information, the fingerprints and the iris are crisscross-shaped, and the human faces are divided horizontally), wherein the four small regions form a matrix form;
s2, extracting eight edges of the four small regions and boundaries of main features by using an edge detection method (a Canny method that is not easily interfered by noise and can detect a true weak edge can be selected for extraction in the edge detection method, and a specific extraction process is the prior art and is not described in detail herein);
s3, calculating the gradient amplitudes of the boundaries of the eight edges and the main features extracted in the step 2 by using a gradient operator (the gradient amplitudes can be calculated by using a Roberts operator, the specific calculation process is the prior art, and the detailed description is omitted), and calculating a quantized average value XP and a quantized value X respectivelyZL；
S4, quantizing the average value XP and the quantized value XZLSubstituting into the formula of quality assessment R to calculate the quality assessment R, R equals XP × 40% + XZL×60％；
S5, comparing the quality evaluation R with a threshold value of 0.8, binarizing, and converting into 1 or 0 to indicate that the precision is met or not met;
and S6, when the quality evaluation R exceeds three times and still does not meet the precision, the microprocessor reminds to carry out acquisition and evaluation of other types of biological identification information.
In a second embodiment, on the basis of the first embodiment, the step of calculating the gradient amplitude by using the gradient operator specifically comprises:
s31, calculating the gradient amplitude of each region respectively by using a gradient operator (Roberts operator can be used for calculation, the specific calculation process is the prior art, and is not detailed here) X1A，A2A，A3A，A4AAnd performing normalization processing to make the gradient amplitude value between 0 and 1, setting the weights of the four regions as 28%, 35%, 22% and 15% according to the importance of the quality evaluation accuracy of the influence of the four regions, and calculating the quantized average value of the boundaries of the four small regions
XP＝(X1L+X2L+X3L+X4L) /4, wherein X1L＝X1A×28％，X2L＝A2A×35％，X3L＝A3A×22％，X4L＝A4A×15％；
S32, comparing the quantized average value XP with a threshold value of 0.3, carrying out binarization, converting the quantized average value XP into 1 or 0 to indicate that the precision is met or not met, and executing the step 6 if the precision is not met;
when S33 and S32 are satisfied, gradient operators are adopted to respectively calculate the gradient amplitude X of the main features in the four small regions1B，A2B，A3B，A4BAnd carrying out normalization processing to set the weights of the four regions to be 20%, 40%, 20% and 20% respectively when the gradient amplitude is between 0 and 1, and calculating the quantized values of the four main characteristics
XZL＝(X1B×20％+A2B×40％+A3B×20％+A4B×20％)/100；
And S34, comparing the quantized average value XP with a threshold value of 0.5, carrying out binarization, converting the quantized average value XP into 1 or 0 to indicate that the precision is met or not met, and executing the step 4 if the precision is met or executing the step 6 if the precision is not met.
When the biological identification information acquisition module is used, the biological identification information acquired by the biological identification information acquisition module is subjected to feature extraction and then transmitted to the microprocessor, and the microprocessor performs evaluation quality analysis on the biological identification information, wherein the evaluation quality analysis specifically comprises the steps of classifying according to feature variables of the biological identification information, further dividing the biological identification information into four small areas X1, X2, X3 and X4, forming a matrix form by the four small areas, and forming eight edges between the adjacent small areas in the row and column directions; extracting eight edges of the four small areas and the boundaries of the main features by adopting an edge detection method; calculating gradient amplitude X of the extracted eight edges by adopting a gradient operator1A，A2A，A3A，A4ANormalization is performed so that the gradient amplitude is between 0 and 1, the weights of the four regions are set to 28%, 35%, 22%, and 15%, respectively, according to the importance of the quality evaluation accuracy of the influence of the four regions, and the quantized average value XP of the boundary of the four small regions is calculated as (X)1L+X2L+X3L+X4L) /4, wherein X1L＝X1A×28％，X2L＝A2A×35％，X3L＝A3A×22％，X4L＝A4A× 15%, comparing the quantized average value XP with the threshold value of 0.3, binarizing, converting into 1 or 0 to indicate that the precision is satisfied or not, immediately enabling the microprocessor to re-collect the biological identification information if the precision is not satisfied, and respectively calculating the gradient amplitude X of the main features in the four small areas by adopting a gradient operator if the precision is satisfied or not, and if the precision is not satisfied1B，A2B，A3B，A4BAnd carrying out normalization processing to set the weights of the four regions to be 20%, 40%, 20% and 20% respectively when the gradient amplitude is between 0 and 1, and calculating the quantized values X of the four main characteristicsZL＝(X1B×20％+A2B×40％+A3B×20％+A4B× 20%)/100, comparing the quantized average value XP with the threshold value of 0.5, binarizing, and immediately re-collecting the biological identification information by the microprocessor if the quantized average value XP is not satisfied, or else, comparing the quantized average value XP with the quantized value XZLSubstituting into the formula of quality assessment R to calculate the quality assessment R, R equals XP × 40% + XZL× 60%, comparing the quality evaluation R with the threshold value of 0.8, binarizing, transmitting to the financial service transaction center through the transmission module under the control of the microprocessor when the quality evaluation R meets the threshold value, immediately enabling the microprocessor to acquire the biological identification information again when the quality evaluation R does not meet the precision after exceeding three times, and reminding the microprocessor to acquire and evaluate other types of biological identification information.

Claims (2)

1. The utility model provides an use biological identification technology modified small amount to exempt from secret payment system, includes biological identification information acquisition module, microprocessor, transmission module, financial service transaction center, payment end, a serial communication port, the biological identification information that biological identification information acquisition module gathered is transmitted to microprocessor after the characteristic is drawed, microprocessor assesses the quality analysis to biological identification information, when satisfying the quality precision, convey to financial service transaction center through transmission module, the biological identification information that prestores in financial service transaction center and the database matches, the output matches the result and sends microprocessor through transmission module, microprocessor control whether allows payment end to carry out secret transaction:
the specific method for evaluating quality analysis of biological identification information comprises the following steps:
s1, obtaining the biological identification information after feature extraction, and further dividing into four small areas X1，X2，X3，X4The four small regions form a matrix form, and eight edges are formed between the adjacent small regions in the row and column directions;
s2, extracting eight edges of the four small regions and the boundaries of the main features by adopting an edge detection method;
s3, calculating the gradient amplitude extracted in the step 2 by adopting a gradient operator, and respectively calculating a quantized average value XP and a quantized value XZL；
S4, calculating a quality assessment R, wherein the formula is R = XP × 40% + XZL×60％；
S5, comparing the quality evaluation R with a threshold value of 0.8, binarizing, and converting into 1 or 0 to indicate that the precision is met or not met;
s6, when the biological identification information is satisfied, the microprocessor controls the biological identification information to be transmitted to the financial service transaction center through the transmission module, and when the biological identification information is not satisfied, the microprocessor acquires the biological identification information again;
the step 3 of calculating the gradient amplitude extracted in the step 2 by using a gradient operator specifically comprises:
s31, calculating the gradient amplitude X of each region by using a gradient operator1A，A2A，A3A，A4ANormalizing to make gradient amplitude between 0-1, setting weights of the four regions as 28%, 35%, 22% and 15%, and calculating the quantized average value of the boundary of the four small regions
XP=（X1L+X2 L+X3 L+X4 L) /4, wherein X1 L =X1A×28％，X2 L =A2A×35％，X3 L =A3A×22％，X4 L =A4A×15％；
S32, comparing the quantized average value XP with a threshold value of 0.3, carrying out binarization, converting the quantized average value XP into 1 or 0 to indicate that the precision is met or not met, and enabling the microprocessor to re-acquire the biological identification information when the quantized average value XP is not met in the step 6;
when S33 and S32 are satisfied, gradient operators are adopted to respectively calculate the gradient amplitude X of the main features in the four small regions1B，A2B，A3B，A4BAnd carrying out normalization processing to set the weights of the four regions to be 20%, 40%, 20% and 20% respectively when the gradient amplitude is between 0 and 1, and calculating the quantized values of the four main characteristics
XZL=（X1B×20％+ A2B×40％ + A3B×20％+ A4B×20％）/100；
S34, quantizing the value XZLComparing with a threshold value of 0.5, binarizing, converting into 1 or 0 to indicate that the precision is met or not met, executing the step 4 when the precision is met, and executing the step 6 when the precision is not met, so that the microprocessor collects the biological identification information again;
when the quality evaluation R does not satisfy the accuracy for more than three times in the step S5, the microprocessor prompts acquisition and evaluation of other types of biometric information.
2. The improved micropayment system according to claim 1, wherein the biometric information comprises fingerprint recognition, face recognition, iris recognition.
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