CN106022782A - Iris payment system - Google Patents
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- CN106022782A CN106022782A CN201610547900.7A CN201610547900A CN106022782A CN 106022782 A CN106022782 A CN 106022782A CN 201610547900 A CN201610547900 A CN 201610547900A CN 106022782 A CN106022782 A CN 106022782A
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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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Abstract
The invention provides an iris payment system. The iris payment system comprises an image acquisition module which is used for acquiring a user iris image, an image preprocessing module which is connected with the image acquisition module and preprocesses the iris image, a feature extraction module which is connected with the data preprocessing module and carries out feature extraction on the preprocessed iris image, a data coding module which is connected with the feature extraction module and encodes and encrypts the iris image feature, and a data transmission module which is connected with the data coding module and transmits the encrypted information, and a cloud server which is used for receiving the encrypted information transmitted by the data transmission module, decrypts and matches the information and returns a matching result to the data transmission module.
Description
Technical field
The present invention relates to electronic payment technical field, particularly to a kind of iris payment system..
Background technology
Commonly used along with pay by mails, its safety, reliability are the most increasingly paid close attention to by people.Pay by mails
Currently mainly problems faced is, pays technological side by mails and faces safety problem, pays by mails and lacks effective supervision, and electronics props up
Pay the identity that cannot effectively confirm payee.The safety problem of E-Payment technology is always the focus of public attention, although respectively
Big payment platform all takes substantial amounts of secrecy provision, cryptographic technique, digital signature technology, numeral creed, digital certificate etc., but
Identity independence, the safety etc. of cryptoguard still suffer from some problems, and safety can not be protected and restriction be paid by mails
Development so that have influence on the development of ecommerce.Traditional identification mode is mainly by the thing of identification marker personal identification
Thing, mainly includes identity tag article and identity tag knowledge.But traditional identification is easily forged, and confirm the thing of identity
Product once lose the identity that is easily stolen.
At present, E-Payment mainly utilizes the modes such as bank card encrypted code, fingerprint, recognition of face to pay.But refer to
There is the problem of accuracy of identification in stricture of vagina and recognition of face, and carries out paying existence by password and arrange simple password and be easily cracked complexity
The problem that password is easily forgotten.
Thus need to propose a kind of new payment mode, the most safe and reliable but also be difficult to loss and forget.
Summary of the invention
The invention provides a kind of iris payment system, to overcome accuracy of identification in existing technology the highest, processing speed is slow
Problem,
The technical scheme that the present invention provides is:
A kind of iris payment system, including:
Image capture module, it is used for gathering client iris image；
Image pre-processing module, it is connected with described image capture module, and iris image is carried out pretreatment；
Characteristic extracting module, it is connected with described image pre-processing module, and pretreated iris image is carried out feature extraction；
Data coding module, it is connected with described characteristic extracting module, and iris image feature is encoded and encrypted；
Data transmission module, it is connected with described data coding module, the information after encryption to be transmitted；
Cloud server, its for receive data transmission module transmission encryption after information, and this information is decrypted,
Join, and matching result is returned to data transmission module.
Preferably, described image capture module is configured to iris scan device.
Preferably, described image pre-processing module carries out the step of Image semantic classification and includes Iris Location, image normalizing
Change and image enhaucament three step.
Preferably, described Iris Location step, first to iris coarse positioning, provides iris Position Approximate in the picture,
Then Daugma calculus or Hough transform method based on rim detection is used to be accurately positioned.
Preferably, described image normalization step uses elastic tape model algorithm.
Preferably, described characteristic extracting module use based on weighted Hamming distance from Algorithm of Iris Recognition carry out rainbow
The feature extraction of film image.
Preferably, described data coding module uses rivest, shamir, adelman such as RSA Algorithm iris image feature to be entered
Row encryption.
Preferably, described cloud server uses the Skyline algorithm of the Map-Reduce after improving to carry out data
Join.
The invention has the beneficial effects as follows: the iris payment system that the present invention provides is at the most local image of general transaction flow
Improved on the basis of the two-step method certification recognition method of pretreatment and remote diagnosis identification.First, this locality is carried out pre-
Imagery exploitation after process Algorithm of Iris Recognition based on Weighted H amming distance extracts iris feature, makes the feature of extraction
More there is practicality and effectiveness.Picture after treatment is obviously reduced compared to original image, reduces the requirement of transmission,
Improve the utilization rate of flow simultaneously.Image after the collection of each warehouse terminal is pretreated, data and consumption information encryption
After reach high in the clouds, encryption uses rivest, shamir, adelman such as RSA Algorithm, therefore ensures that being perfectly safe of user profile and property.
Information is issued to after carrying out data deciphering beyond the clouds child servers uses map-reduce algorithm to carry out efficient matchings, after coupling
Return message, successful then complete the delivery operation of this user, failed then feed back to terminal and re-start collection.Simultaneously in order to improve
Systematic function, provides the user preferably experience, have employed asynchronous response technology in cloud computing system.
Accompanying drawing explanation
Fig. 1 is iris payment system of the present invention each module connection structure schematic diagram.
Detailed description of the invention
The present invention is described in further detail below in conjunction with the accompanying drawings, to make those skilled in the art with reference to description literary composition
Word can be implemented according to this.
As it is shown in figure 1, the invention provides a kind of iris payment system, including iris image acquiring module 110, its configuration
For iris scan device, obtain client's eyes image with friendly interactive mode.The eyes image photographed due to iris scan device
Contain the most unnecessary information, and requirement can not be met at aspects such as definitions, need to carry out processing operation to it.Eye
After image procossing, thus isolate iris image.
Described iris image acquiring module 110 is connected with image pre-processing module 120, is sent by the eyes image collected
To image pre-processing module 120, carry out the pretreatment of image.The pretreatment of image comprises Iris Location, image normalization, image
Strengthen three steps.
The image collected contains pupil, iris and sclera etc., is positioned iris by Algorithm of Iris Recognition.One
As think that what the inner and outer boundary of iris can approximate is fitted with circle, inner circle represents the convenient of pupil and iris, and cylindrical is then
Iris and the border of sclera.
Traditional iris locating method has the integro-differential operator of Daugman and the Hough based on rim detection of Wildes
Converter technique.Improved method is exactly to iris coarse positioning, provides iris Position Approximate in the picture, provides effectively for fine positioning
Parameter, the most effectively reduce seeking scope, then use traditional method that iris is carried out fine positioning, reduce greatly
Positioning time.The iris of different people varies in size, and in varied situations, size also can change same iris.Pupil can be along with
The change of ambient light photograph and expand or shrink, thus affect the size of iris；During collection, eyes are direct with the distance of collecting device
Have influence on the size of pupil image.This elastic deformation of iris will affect recognition result, and different size of iris does not has yet
Way carries out match cognization, and the normalization of iris can correct these scaling distortions effectively.
Now widely used method for normalizing is elastic tape (Rubber-sheet) model that professor Daugman proposes,
This model is assumed to be one iris and has pliability and isotropic elastic model, by the iris region linear expansion of annular
It is that a rectangular area with fixed size processes.Point owing to calculating is not integer under normal circumstances, nothing
Method corresponds to the annular region of iris, therefore uses bilinear interpolation to process it, is finally normalized.
Image pre-processing module 120 is connected with characteristic extracting module 130, and pretreated image is carried out feature extraction.
A width texture image can be seen as, then the side of many texture analysiss through the pretreated image of image pre-processing module 120
Method can be adopted to extract iris feature.Have and several compare typical method multi-Channel Gabor Filtering and two-dimensional wavelet transformation.
Based on weighted Hamming distance from Algorithm of Iris Recognition be a kind of good algorithm.First, strange according to real part even symmetry, imaginary part
The characteristic of symmetrical Gabor filter, uses different scale, direction imaginary part odd symmetry Gabor filter to be identified iris storehouse；
Then, according to etc. error rate (EER) and the highest correct recognition rata (CRR) choose the Gabor filter in suitable yardstick and direction, adopt
With based on weighted Hamming distance from carrying out iris identification, wherein, the calculating of weights uses analytic hierarchy process (AHP)；Finally, the party is used
Different iris storehouses is identified by method.
Two-dimensional Gabor converts
Have by the two-dimensional Gabor filter of Gabor function formation and obtain optimal partial in spatial domain and frequency domain simultaneously
Characteristic, believes therefore, it is possible to describe the partial structurtes corresponding to spatial frequency (yardstick), locus and set direction well
Breath.The frequency of Gabor filter and direction represent close to human visual system for frequency and the expression in direction, and they are normal
It is used for texture representation and description.In image processing field, Gabor filter is a linear filtering for rim detection
Device.In spatial domain, the Gabor filter of one 2 dimension is a sinusoidal plane wave and the product of gaussian kernel function.Gabor filter
It is self similarity, say, that all Gabor filter can produce through expanding and rotating from a morther wavelet.Actual
In application, Gabor filter can extract correlated characteristic at the different scale of frequency domain on different directions.
The general type of two-dimensional complex number Gabor is:
Wherein:,For the scale parameter of wave filter, F is frequency, and θ is direction.
Can be decomposed into real part even symmetry and odd symmetric two wave filter of imaginary part:
Can be using two-dimensional Gabor as the band filter in direction, the low pass filter in vertical direction, changing F and θ can be fine
Extract corresponding texture information, the textural characteristics being therefore very suitable for extracting iris carries out iris identification.
Daugman after Gabor Phase information processes, extracts the local phase information textural characteristics as iris to iris image,
Then encoding characteristic point, this method can resolve into two parts, i.e. real part even symmetric filter and imaginary part odd symmetry
Wave filter.If the amplitude after Chu Liing is more than 0, it is encoded to 1, is otherwise 0.
Two-dimensional Gabor filter parameter value
,For the scale factor of filtering window, they determine the sphere of action of wave filter.Order,
As u2, functional value varies less, and decay has exceeded 98.168%；Therefore u=2 can be taken.
(1) according to the odd symmetry of wave filter, direction θ is chosen for 0 °, 45 °, 90 °, 135 °；
(2) n=1 is taken, u=2.When frequency F takes 0.125,0.0625,0.0417,0.03125, yardstick,It is 2,2 √ 2,3
√2、4√2。
Different scale, direction filter result as shown in table 1.
Table 1
Owing to the recognition performance of the wave filter in 90 ° of directions is the poorest, so not considering.
Weighted Hamming distance from recognition methods
The seventies in last century, Saaty proposed analytic hierarchy process (AHP) (Analytic Hierarchy Process, AHP), and it will be with
The relevant Factor Decomposition of decision-making becomes the levels such as target, criterion, scheme, carries out analysis qualitatively and quantitatively the most again.Step analysis
Method basic ideas are the most the same to the judge process of certain decision problem with people.Analytic hierarchy process (AHP) mainly has following two
Individual feature: 1) reasonably qualitative and quantitative is combined；2) according to psychology and thinking rule by the process number of decision-making,
Stratification.So analytic hierarchy process (AHP) has extraordinary practicality and effectiveness in solution complicated decision-making problems, this also makes it
Every field worldwide is widely used, as economical, manage, educate, transport, energy distribution and utilizing
Deng.
The weights finally obtained are respectively as follows: 0.7,0.2,0.1.
Characteristic extracting module 130 is connected with data coding module 140, by characteristic extracting module 130 by having in iris
Effect information is extracted, and at this by obtaining the characteristic information of user, is then passed through data coding module 140 and compiles this information
Code is also encrypted, in order to stores and transmits.This feature coding need the highest safety and representative can with guarantee to pay
Row.
Data coding module 140 is connected with data transmission module 150, with the iris data that will obtain and consumption information encryption
After reach cloud server 160, encryption uses rivest, shamir, adelman such as RSA Algorithm, therefore ensures that user profile and property
It is perfectly safe.Information is issued to after server 160 carries out data deciphering beyond the clouds child servers 170 uses map-reduce to calculate
Method carries out efficient matchings, returns message after coupling, successful then complete the delivery operation of this user, failed then feed back to terminal again
It is acquired.Simultaneously in order to improve systematic function, provide the user preferably experience, cloud computing system have employed asynchronous sound
Answer technology.When sending data or request data to cloud server 160 due to terminal, server may carry out substantial amounts of number
According to computing, it is possible that have longer response time, in order to be able to more preferable Consumer's Experience, after sending request, service
Before device response, terminal can do the operation of some simple feedback acknowledgments, also enhances safety and convenience simultaneously.
Owing to data base is huge, user profile is more, so Data Matching will occupy bigger ratio in response time
Weight, is improved search algorithm Skyline of Map/Reduce according to practical situation.Map-Reduce is the most more to flow
The multiple programming framework of row, for the parallel computation of large-scale dataset, comprises a Master node and multiple in framework
Slave node, Master node is responsible for task scheduling and the distribution of whole framework, and Slave node is responsible for execution and is assigned to self
Task.In Map-Reduce framework, the processing procedure of an operation mainly includes two stages: be used for dividing initial data
With Map stage and the Reduce stage of generation final result producing intermediate object program.User can be by simply configuring Map-
Per function and Reducer function realize the executed in parallel to original task.
Use search algorithm Skyline of the Map/Reduce after improving: due to the variation in the geographical position of most people
Less, can be according to the quantity of population distribution Cloud Server, more for population by for whole nation each province segmentation service device
City is such as: Beijing/Shanghai/Shenzhen etc., divides more Slave node server and carries out Map mapping.Shop often has simultaneously
Frequent customer, so the iris data weight once retrieved also will improve, by the number that preferential retrieval these two aspects weight is higher
According to improving matching efficiency.Background data base uses Document image analysis MongoDb to ensure the high efficiency of data retrieval simultaneously
And extensibility, use NoSQL sentence retrieval data base, centered by terminal sites, the iris letter of priority match immediate vicinity
Breath, improves matching efficiency with this.Delivery operation is i.e. completed after the match is successful.
The iris payment system that the present invention provides is known at the most local Image semantic classification of general transaction flow and remote diagnosis
Improved on the basis of other two-step method certification recognition method.First, this locality is carried out pretreated imagery exploitation base
Algorithm of Iris Recognition in Weighted H amming distance extracts iris feature, makes the feature of extraction more have practicality and have
Effect property.Picture after treatment is obviously reduced compared to original image, reduces the requirement of transmission, improves the profit of flow simultaneously
By rate.Reaching high in the clouds after image after the collection of each warehouse terminal is pretreated, data and consumption information encryption, encryption uses
Rivest, shamir, adelman such as RSA Algorithm, therefore ensures that being perfectly safe of user profile and property.After carrying out data deciphering beyond the clouds
Information is issued to child servers uses map-reduce algorithm to carry out efficient matchings, returns message after coupling, successful then complete
The delivery operation of this user is failed then feed back to terminal and re-start collection.Simultaneously in order to improve systematic function, provide the user
Preferably experience, cloud computing system have employed asynchronous response technology.
The above, be only presently preferred embodiments of the present invention, and the present invention not makees any pro forma restriction, this
Skilled person utilizes the technology contents of the disclosure above to make a little simple modification, equivalent variations or modification, all falls within this
In bright protection domain.
Claims (8)
1. an iris payment system, it is characterised in that including:
Image capture module, it is used for gathering client iris image；
Image pre-processing module, it is connected with described image capture module, and iris image is carried out pretreatment；
Characteristic extracting module, it is connected with described image pre-processing module, and pretreated iris image is carried out feature extraction；
Data coding module, it is connected with described characteristic extracting module, and iris image feature is encoded and encrypted；
Data transmission module, it is connected with described data coding module, the information after encryption to be transmitted；
Cloud server, its for receive data transmission module transmission encryption after information, and this information is decrypted,
Join, and matching result is returned to data transmission module.
Iris payment system the most according to claim 1, it is characterised in that described image capture module is configured to iris and sweeps
Retouch device.
Iris payment system the most according to claim 1, it is characterised in that it is pre-that described image pre-processing module carries out image
The step processed includes Iris Location, image normalization and image enhaucament three step.
Iris payment system the most according to claim 3, it is characterised in that described Iris Location step is first thick to iris
Location, provides iris Position Approximate in the picture, then uses Daugma calculus or Hough based on rim detection
Converter technique is accurately positioned.
5. according to the iris payment system described in claim 3 or 4, it is characterised in that described image normalization step uses rubber
Apron model algorithm.
Iris payment system the most according to claim 5, it is characterised in that described characteristic extracting module uses based on weighting
Hamming distance from Algorithm of Iris Recognition carry out the feature extraction of iris image.
Iris payment system the most according to claim 6, it is characterised in that described data coding module uses asymmetric adding
Iris image feature is encrypted by close algorithm such as RSA Algorithm.
Iris payment system the most according to claim 7, it is characterised in that described cloud server uses after improving
The Skyline algorithm of Map/Reduce carries out Data Matching.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106529464A (en) * | 2016-10-28 | 2017-03-22 | 信利光电股份有限公司 | Iris characteristic acquisition module group and electronic device |
| CN107195079A (en) * | 2017-07-20 | 2017-09-22 | 长江大学 | A kind of dining room based on iris recognition is swiped the card method and system |
| CN107392607A (en) * | 2017-07-05 | 2017-11-24 | 佛山杰致信息科技有限公司 | Payment system |
| CN107423971A (en) * | 2017-07-05 | 2017-12-01 | 佛山杰致信息科技有限公司 | safety payment system |
| CN109587130A (en) * | 2018-11-29 | 2019-04-05 | 贵州航天云网科技有限公司 | One kind being based on the consistent integrated operation support system of RTI space-time |
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| EP1842152B1 (en) * | 2005-01-26 | 2011-01-05 | Honeywell International Inc. | A distance iris recognition system |
| CN102710613A (en) * | 2012-05-14 | 2012-10-03 | 西安电子科技大学 | Signcryption method of biological features of a plurality of receivers |
| CN105335665A (en) * | 2015-10-28 | 2016-02-17 | 广东欧珀移动通信有限公司 | Encryption method, encryption system, decryption method and decryption system of picture |
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| CN1760887A (en) * | 2004-10-11 | 2006-04-19 | 中国科学院自动化研究所 | The robust features of iris image extracts and recognition methods |
| EP1842152B1 (en) * | 2005-01-26 | 2011-01-05 | Honeywell International Inc. | A distance iris recognition system |
| CN102710613A (en) * | 2012-05-14 | 2012-10-03 | 西安电子科技大学 | Signcryption method of biological features of a plurality of receivers |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106529464A (en) * | 2016-10-28 | 2017-03-22 | 信利光电股份有限公司 | Iris characteristic acquisition module group and electronic device |
| CN107392607A (en) * | 2017-07-05 | 2017-11-24 | 佛山杰致信息科技有限公司 | Payment system |
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| CN107195079A (en) * | 2017-07-20 | 2017-09-22 | 长江大学 | A kind of dining room based on iris recognition is swiped the card method and system |
| CN109587130A (en) * | 2018-11-29 | 2019-04-05 | 贵州航天云网科技有限公司 | One kind being based on the consistent integrated operation support system of RTI space-time |
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