CN110189138A - A kind of certification payment system based on bio-identification - Google Patents
A kind of certification payment system based on bio-identification Download PDFInfo
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- CN110189138A CN110189138A CN201910467420.3A CN201910467420A CN110189138A CN 110189138 A CN110189138 A CN 110189138A CN 201910467420 A CN201910467420 A CN 201910467420A CN 110189138 A CN110189138 A CN 110189138A
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- biometric information
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- G—PHYSICS
- 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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/70—Multimodal biometrics, e.g. combining information from different biometric modalities
Abstract
A kind of certification payment system based on bio-identification of the invention, the biometric information of biometric information acquisition module acquisition is transferred to microprocessor, microprocessor is sent to financial services transactions center by transmission module, financial services transactions are matched using the biometric information prestored in bi-directional matching algorithm and database, and through similarity algorithm relatively after output matching result through transmission module be sent to microprocessor, microprocessor controls whether that payment terminals is allowed to carry out exempting from close transaction.The present invention treats matched biometric information and divides region, Boundary Extraction, is set as two groups of Feature Segmentations, using the matching of bi-directional matching algorithm fusion similarity algorithm, analysis, when one group of Feature Segmentation successful match, analyze whether an edge is overlapped, when two groups of Feature Segmentation successful match, four boundaries for analyzing four zonules are overlapped two-by-two, the overlapping margins of main feature, finally use similarity algorithm global analysis, with this efficiently, accurately complete certification.
Description
Technical field
The present invention relates to bio-identification payment technology field, especially a kind of certification payment system based on bio-identification.
Background technique
With the development of intellectualization times, using online payment, the mode of mobile-phone payment is more and more common, needs user defeated
Enter the payment cipher of 6 bit digitals or show two dimensional code to complete payment flow, paid in public using mobile phone, is easy
By stealing passwords such as pedestrians or brush two dimensional code is stolen, there are security risks.
In order to simplify user's operation, improve payment safety, there is the means of payment based on biological identification technology, such as refer to
Line identification, recognition of face, iris recognition, hand vein recognition etc. acquire biometric information by biometric information acquisition module
It is transferred to microprocessor, microprocessor is sent to financial services transactions center by transmission module, and financial services transactions will be biological
The biometric information prestored in identification information and database is matched, successful match, namely by verifying, by matching result
Microprocessor is re-send to through transmission module, and microprocessor control allows payment terminals exempt from close transaction, and financial services transactions
The biometric information amount prestored in the database at center is very big, how to realize it is efficient, it is accurate matching be improve payment efficiency,
One of the technical issues of safety.
Therefore the present invention provides a kind of new scheme to solve the problems, such as this.
Summary of the invention
In view of the deficienciess of the prior art, purpose of the present invention is to provide a kind of, the certification based on bio-identification is paid
System can be improved effectively by the way that region, bi-directional matching algorithm fusion similarity algorithm are clustered, divided to biometric information
Matched speed, accuracy.
To achieve the goals above, the present invention is to realize by the following technical solutions: being adopted including biometric information
Collect module, microprocessor, transmission module, financial services transactions center, payment terminals, which is characterized in that the biometric information
The biometric information of acquisition module acquisition is transferred to microprocessor, and microprocessor is sent to financial service by transmission module and hands over
Easy center, financial services transactions are matched using the biometric information prestored in bi-directional matching algorithm and database, and are passed through
Similarity algorithm exports matching result more afterwards and is sent to microprocessor through transmission module, and microprocessor controls whether to allow to pay
End carries out exempting from close transaction:
It is described that matched specific method step is carried out using the biometric information prestored in bi-directional matching algorithm and database
It is rapid as follows:
S1, the biometric information after obtaining feature extraction to be matched are (raw according to the characteristic variable of biometric information
Type, the size of object identification information) classify, and it is further divided into four zonules zonule X1, X2, X3, X4, four
Matrix form is constituted, forms eight edges between the zonule adjacent on column direction of being expert at；
S2, using clustering algorithm step by step to the biometric information prestored in database by characteristic variable biometric information
Type, size carry out two-stage cluster, determine meet type, size biometric information data set be doubtful bio-identification believe
Cease YS0；
S3 extracts eight edges of four zonules, the boundary of main feature using edge detection method；
Four zonule X1, X2, X3 are arranged in S4, and the edge of X4, the boundary of main feature, which are characterized, is segmented A, a, B, b, C,
C, D, d；
S5, to Feature Segmentation A, a, B, b, C, c, D, d carries out being segmented into A, a, D, d and B, b, C, and c is calculated using bi-directional matching
Method is simultaneously by A, a, D, d/B, b, C, and c is matched with biometric information YS0/YS3, similarity algorithm analysis A, a/D, and d
It when with success, then analyzes whether an edge is overlapped, when not being overlapped, deletes the doubtful biometric information in this matching
YS3, when coincidence, execute down；
Similarity algorithm analyzes B, b, C, when c successful match, then analyzes whether an edge is overlapped, and when not being overlapped, deletes
The doubtful biometric information YS6 in this matching, when coincidence, execute down
Whether the boundary of S6, four boundaries, main feature of analyzing four zonules are overlapped, and when not being overlapped, execute step
5, when coincidence, uses similarity algorithm global analysis again, exports matching result, is sent to microprocessor, micro- place through transmission module
Reason device controls whether that payment terminals is allowed to carry out exempting from close transaction.
Due to the use of above technical scheme, the invention has the following advantages over the prior art:
1, by clustering to biometric information two-stage in financial services transactions central database, it is divided into biometric information
Data set YS0 avoids searching for entire database, to improve the efficiency of 1:N identification；
2, region, eight sides of the edge detection method to four zonules are divided by treating matched biometric information
Edge, main feature boundary extract, extract back boundary be further arranged to two groups of Feature Segmentations, later use bi-directional matching
Algorithm matches two groups of Feature Segmentations with biometric information YS0YS0/YS3, to further increase matched speed, phase
When analyzing one group of Feature Segmentation successful match like degree algorithm, then analyze whether an edge is overlapped, it is matched to further increase
Accuracy, four boundaries for analyzing four zonules are overlapped two-by-two, the overlapping margins of main feature when, use similarity operator again
Method global analysis exports accurate matching result, then feeds back microprocessor, and microprocessor controls whether that payment terminals is allowed to carry out
Exempt from close transaction, accurately completion certification efficient with this.
Detailed description of the invention
Fig. 1 is entire block diagram of the present invention.
Fig. 2 is overall step flow chart of the present invention.
Fig. 3 is the step flow chart of bi-directional matching of the present invention.
Fig. 4 is the step flow chart of two-stage of the present invention cluster.
Specific embodiment
For the present invention aforementioned and other technology contents, feature and effect, in following cooperation with reference to figures 1 through attached drawing 4
To in the detailed description of embodiment, can clearly present.The structure content being previously mentioned in following embodiment is with specification
Attached drawing is reference.
In order to verify the feasibility of this method and the effect of actual use, analyzing examples are carried out below and verify this method.
Embodiment one, a kind of certification payment system based on bio-identification, the biometric information acquisition module acquisition
Biometric information (fingerprint recognition, recognition of face, iris recognition can specifically be identified by corresponding identification module)
It is transferred to microprocessor (can be single-chip microcontroller), microprocessor is transmitted by transmission module (can be GPRS module, 3G/4G communication network)
To financial services transactions center (such as mobile financial services transactions center), financial services transactions pass through poly- to biometric information
Class divides region, is double using the biometric information progress prestored in bi-directional matching algorithm fusion similarity algorithm and database
To matching, further compare after successful match and through similarity algorithm, finally output matching result is sent to micro- through transmission module
Processor, microprocessor control whether that permission payment terminals carry out exempting from close transaction energy, effectively improve matched speed, accuracy:
It is described that matched specific method step is carried out using the biometric information prestored in bi-directional matching algorithm and database
It is rapid as follows:
S1, the biometric information after obtaining feature extraction to be matched are (raw according to the characteristic variable of biometric information
Object identification information: structure type-circularity of fingerprint, face, iris, such as fingerprint are arch, account arch, left its shape, right dustpan, spiral shell
Shape, face is standard, O shape, rectangular, triangle, diamond shape, cycle of sixty years type, elongated, and iris is almond-eyed, slim eye, gets deeply stuck in eye, thickness
Convex eye, lower extension eye, hypertropia, the size of fingerprint, face, iris, such as standard, large and small) classify, and further
Four zonule X1, X2, X3 are divided into, X4 (can be drawn by the primary biological feature of biometric information, fingerprint, iris cross
Point, face horizontal division), four zonules constitute matrix form, are formed between the zonule adjacent on column direction of being expert at
Eight edges；
S2, using clustering algorithm step by step to the biometric information prestored in database by characteristic variable biometric information
Type, size carry out two-stage cluster, determine meet type, size biometric information data set be doubtful bio-identification believe
YS0 is ceased, the biometric information prestored in database can also be known by characteristic variable biology using clustering algorithm step by step to be preparatory
Type, the size of other information carry out two-stage cluster, only need to call herein；
S3 extracts eight edges of four zonules, the boundary of main feature using edge detection method (optional
It takes and is not susceptible to noise jamming in edge detection method, be able to detect that the Canny method at real weak edge extracts, have
The extraction process of body is the prior art, and this will not be detailed here)；
S4, is arranged four zonule X1, X2, X3, and (such as by region: the face of face are special for the edge of X4, main feature
Sign makees main feature, the pupil of iris, eyelid, the white of the eye, eyelashes and makees main feature, the central point of fingerprint and with the three of central point work
A uniform outer circle makees main feature) boundary be characterized segmentation A, a, B, b, C, c, D, d；
S5, to Feature Segmentation A, a, B, b, C, c, D, d carries out being segmented into A, a, D, d and B, b, C, and c is calculated using bi-directional matching
Method is simultaneously by A, a, D, and d/B, b, C, c is matched with biometric information YS0/YS3, to further increase matched speed,
Similarity algorithm (can be BlobTracking algorithm, specific similar comparison procedure is the prior art, and this will not be detailed here) point
A, a/D are analysed, when d successful match, then analyzes whether an edge is overlapped, when not being overlapped, is deleted described doubtful in this matching
Biometric information YS3, when coincidence, execute down；
Similarity algorithm analyzes B, b, C, when c successful match, then analyzes whether an edge is overlapped, and when not being overlapped, deletes
The doubtful biometric information YS6 in this matching, when coincidence, execute down
Whether the boundary of S6, four boundaries, main feature of analyzing four zonules are overlapped, and when not being overlapped, execute step
5, when coincidence, uses similarity algorithm global analysis again, exports accurate matching result, feeds back micro- place again through transmission module
Device is managed, microprocessor controls whether that payment terminals is allowed to carry out exempting from close transaction.
Embodiment two, on the basis of example 1, the step S4's method particularly includes:
The Feature Segmentation A of S41, zonule X1, a are matched with biometric information YS0, similarity algorithm analysis matching
When success, determine to include Feature Segmentation A, the biometric information YS0 of a is doubtful biometric information YS1；
The Feature Segmentation D of zonule X4, d are matched with biometric information YS0, and similarity algorithm analyzes successful match
When, determine to include Feature Segmentation D, the biometric information YS0 of d is doubtful biometric information YS2；
S42, when a coincident of biometric information YS1, YS2, zonule X1, zonule X4 successful match,
Determine that the biometric information YS0 comprising Feature Segmentation A, D is doubtful biometric information YS3, otherwise stops this matching, delete
Except doubtful biometric information YS1, YS2 in this matching, new root of laying equal stress on is known according to the biology comprising Feature Segmentation A is obtained
Other information YS0 is doubtful biometric information YS1, YS2 until edge matching success；
The Feature Segmentation B of S42, zonule X2, b are matched with biometric information YS3, similarity algorithm analysis matching
When success, determine to include Feature Segmentation B, the biometric information YS3 of b is doubtful biometric information YS4；
The Feature Segmentation C of zonule X3, c are matched with biometric information YS3, and similarity algorithm analyzes successful match
When, determine to include Feature Segmentation C, the biometric information YS3 of c is doubtful biometric information YS5, determines to include Feature Segmentation
B, b, C, the biometric information YS0 of c are doubtful biometric information YS6；
S43, when other two coincident of biometric information YS3, YS6, judgement includes Feature Segmentation A,
A, B, b, C, c, D, the biometric information YS0 of d are biometric information YS7,
Embodiment three, on the basis of example 1, the step S2 carry out two-stage cluster method particularly includes:
S21 carries out level-one cluster using type of the K- means clustering algorithm to biometric information；
S22, on the basis of step S21, using K- means clustering algorithm to level-one cluster after biometric information into
The second level of row size clusters；
S23, judgement meets type, the biometric information data set of size is doubtful biometric information YS0 namely right
Biometric information in database is classified, and biometric information data set is divided into, can to avoid searching for entire database,
To improve the efficiency of 1:N identification.
The present invention carry out using when, biometric information acquisition module acquisition biometric information be transferred to it is micro-
Processor, microprocessor are sent to financial services transactions center by transmission module, and financial services transactions apply K- mean value first
Clustering algorithm carries out level-one cluster to the type of biometric information, reapplies K- means clustering algorithm to the life after level-one cluster
Object identification information carries out the second level cluster of size, judgement meets type, the biometric information data set of size is doubtful biology
Identification information YS0, namely classify to the biometric information in database, is divided into biometric information data set, can be with
It avoids searching for entire database, to improve the efficiency of 1:N identification；And divided according to the characteristic variable of biometric information
Class, and be further divided into four zonules X1, X2, X3, X4, four zonules and constitute matrix forms, be expert at on column direction
Adjacent zonule between form eight edges, using edge detection method to eight edges of four zonules, main special
The boundary of sign extracts, extraction back edge is arranged, the boundary of main feature is characterized segmentation A, a, B, b, C, c, D, d, use
Bi-directional matching algorithm is simultaneously by A, a, D, d/B, b, C, and c is matched with biometric information YS0/YS3, to further increase
The speed matched, similarity algorithm analyzes A, a/D, when d successful match, then analyzes whether an edge is overlapped, and when not being overlapped, deletes
The doubtful biometric information YS3 in this matching；Similarly analyze B, b, C, c；The four of four zonules is analyzed when coincidence
A boundary, main feature boundary whether be overlapped, when not being overlapped, match again, when coincidence uses similarity algorithm whole again
Analysis, exports accurate matching result, feeds back microprocessor again through transmission module, microprocessor controls whether to allow payment terminals
It carries out exempting from close transaction, accurately completion certification efficient with this.
Claims (4)
1. a kind of certification payment system based on bio-identification, including biometric information acquisition module, microprocessor, transmission mould
Block, financial services transactions center, payment terminals, which is characterized in that the bio-identification of the biometric information acquisition module acquisition
Information is transferred to microprocessor, and microprocessor is sent to financial services transactions center by transmission module, and financial services transactions are adopted
Matched with the biometric information prestored in bi-directional matching algorithm and database, and through similarity algorithm relatively after output
It is sent to microprocessor through transmission module with result, microprocessor controls whether that payment terminals is allowed to carry out exempting from close transaction:
It is described that matched specific method step is carried out such as using the biometric information prestored in bi-directional matching algorithm and database
Under:
S1, the biometric information after obtaining feature extraction to be matched, according to the characteristic variable of biometric information, (biology is known
Type, the size of other information) classify, and be further divided into four zonules X1, X2, X3, X4, four zonules and constitute
Matrix form forms eight edges between the zonule adjacent on column direction of being expert at；
S2, using clustering algorithm step by step to the biometric information prestored in database by the class of characteristic variable biometric information
Type, size carry out two-stage cluster, and judgement meets type, the biometric information data set of size is doubtful biometric information
YS0；
S3 extracts eight edges of four zonules, the boundary of main feature using edge detection method；
Four zonule X1, X2, X3 are arranged in S4, and the edge of X4, the boundary of main feature, which are characterized, is segmented A, a, B, b, C, c, D,
d；
S5, to Feature Segmentation A, a, B, b, C, c, D, d carries out being segmented into A, a, D, d and B, and b, C, c will using bi-directional matching algorithm
A, a, D, d/B, b, C, c are matched with biometric information YS0/YS3, and similarity algorithm analyzes A, a/D, when d successful match,
It analyzes whether an edge is overlapped again, when not being overlapped, deletes the doubtful biometric information YS3 in this matching, be overlapped
When execute down；
Similarity algorithm analyzes B, b, C, when c successful match, then analyzes whether an edge is overlapped, when not being overlapped, deletes this
The doubtful biometric information YS6 in matching, when coincidence, execute down；
Whether the boundary of S6, four boundaries, main feature of analyzing four zonules are overlapped, and when not being overlapped, execute step 5, weight
Similarity algorithm global analysis is used when conjunction again, matching result is exported, is sent to microprocessor, microprocessor through transmission module
It controls whether that payment terminals is allowed to carry out exempting from close transaction.
2. a kind of certification payment system based on bio-identification according to claim 1, which is characterized in that the step S5
Method particularly includes:
The Feature Segmentation A of S41, zonule X1, a are matched with biometric information YS0, and similarity algorithm analyzes successful match
When, determine to include Feature Segmentation A, the biometric information YS0 of a is doubtful biometric information YS1；
The Feature Segmentation D of zonule X4 is matched with biometric information YS0, when similarity algorithm analyzes successful match, is sentenced
It surely include Feature Segmentation D, the biometric information YS0 of d is doubtful biometric information YS2；
S42, when a coincident of biometric information YS1, YS2, zonule X1, zonule X4 successful match determine
Biometric information YS0 comprising Feature Segmentation A, D is doubtful biometric information YS3, otherwise stops this matching, deletes this
Doubtful biometric information YS1, YS2 in secondary matching, new root of laying equal stress on are believed according to the bio-identification comprising Feature Segmentation A is obtained
Ceasing YS0 is doubtful biometric information YS1, YS2 until edge matching success；
The Feature Segmentation B, b of S43, zonule X2 are matched with doubtful biometric information YS3, similarity algorithm analysis matching
When success, determine to include Feature Segmentation B, the biometric information YS3 of b is doubtful biometric information YS4；
The Feature Segmentation C of zonule X3, c is matched with biometric information YS3, when similarity algorithm analyzes successful match,
Determine to include Feature Segmentation C, the biometric information YS3 of c is doubtful biometric information YS5；
S44, when a coincident of biometric information YS4, YS5, zonule X2, zonule X3 successful match determine
Biometric information YS3 comprising Feature Segmentation B, C is doubtful biometric information YS6, otherwise stops this matching, deletes this
Doubtful biometric information YS4, YS5 in secondary matching, new root of laying equal stress on are believed according to the bio-identification comprising Feature Segmentation B is obtained
Ceasing YS3 is doubtful biometric information YS4, YS5 until edge matching success；
S45, when other two coincident of biometric information YS3, YS6, determine include Feature Segmentation A, a, B, b, C,
C, the biometric information YS6 of D, d are to need matched biometric information.
3. a kind of certification payment system based on bio-identification according to claim 1, which is characterized in that the step S2
Carry out two-stage cluster method particularly includes:
S21 carries out level-one cluster using type of the K- means clustering algorithm to biometric information；
S22 carries out the biometric information after level-one cluster using K- means clustering algorithm big on the basis of step S21
Small second level cluster；
S23, judgement meets type, the biometric information data set of size is doubtful biometric information YS0.
4. a kind of according to claim 1, certification payment system based on bio-identification described in 2,3, which is characterized in that the life
Object identification information includes fingerprint recognition, recognition of face, iris recognition.
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Application publication date: 20190830 |