CN104899493B - A kind of new examination face authentication system - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
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- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
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- G06V40/169—Holistic features and representations, i.e. based on the facial image taken as a whole
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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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Abstract
The present invention relates to Internet of Things areas of information technology, more particularly to a kind of new examination face authentication system, harvester is verified including data acquisition device, data processing equipment, cloud storage service device and multiple admissions, data acquisition device is established data by wired or wireless internet and data processing equipment and communicated to connect, data processing equipment is established data by wired or wireless LAN and cloud storage service device and communicated to connect, data acquisition device gathers character information and image information and generates gathered data, sends in real time to data processing equipment；Data processing equipment according to the region affiliation of gathered data, by corresponding territorial classification database in the long-range client database of acquired data storage to cloud storage service device；The present invention is based on data cloud center and technology of Internet of things, substantially increases recognition efficiency, improves anti-counterfeiting performance.
Description
Technical field
The present invention relates to Internet of Things areas of information technology, and in particular to a kind of new examination face authentication system.
Background technology
It is existing examination flow be all student's standard textual criticism etc. carry out identification after just can admission, it is this examination flow deposit
In 2 drawbacks：First, pupilage identifies problem, the flow is more difficult to determine whether examinee, easily occurs impersonating phenomenon；Two
It is the inadequate hommization of the flow, examinee easily omits admission card for entrance examination, or passes through other numerous and diverse formalities.So both consume time, nothing
Doubt and unnecessary psychological burden is added to examinee.
Above mentioned traditional personal identification method mainly includes two aspects：1. identity article, as identity card,
Atm card, key etc.；2. identity knowledge, such as user name, password.Distinct disadvantage be present in these traditional identity recognition methods：It is individual
People's article is lost, and personal information is forged, even personation.For the above situation, people need to find more personal
Recognition methods, such as fingerprint recognition, recognition of face.
Current Internet of Things, cloud computing, mode identification technology develop rapidly, and on the other hand various examinations emerge in an endless stream, and take an examination
Personnel are numerous, personnel's face data aboundresources of taking an examination, therefore how to be solved using new technology, biometric image, face resource etc.
Exam information identification problem has seemed more and more important.
The content of the invention
Solves above-mentioned technical problem, the invention provides a kind of new examination face authentication system, based in data cloud
The heart and technology of Internet of things, recognition efficiency is substantially increased, improve anti-counterfeiting performance.
In order to achieve the above object, the technical solution adopted in the present invention is a kind of new examination face authentication system,
Harvester, data acquisition device are verified including data acquisition device, data processing equipment, cloud storage service device and multiple admissions
Data are established by wired or wireless internet and data processing equipment to communicate to connect, data processing equipment passes through wired or wireless
LAN establishes data communication connection with cloud storage service device,
Data acquisition device gathers character information and image information and generates gathered data, sends to data processing fill in real time
Put；Data processing equipment according to the region affiliation of gathered data, the remote port by acquired data storage to cloud storage service device
In database in corresponding territorial classification database；
Multiple admission checking harvester dispersed distributions are in regional ownership place admission check post, admission checking collection dress
Put collection Data Concurrent to be tested and deliver to cloud storage service device, cloud storage service device is compared the data to be tested with gathered data
It is right, judge the data to be tested region affiliation whether matched with the territorial classification database where corresponding gathered data,
Checking information is generated with result, to judge that can the data to be tested obtain verifying authorization, if so, then further passing through 1 ratio than N
Compared with method to knowing whether data to be tested match with gathered data, and then obtain whether the data to be tested are returned by the region
The admission checking in possession.
Further, described 1 comparison method than N is specially：Cloud storage service device is by 1 data to be tested and N number of collection number
It is compared according to by the face recognition algorithms of characteristic weighing, the face recognition algorithms of this feature weighting comprise the following steps：It is first
Facial image is first resolved into using wavelet transformation by high-low frequency weight, then carrying out principal component analysis (PCA) to different components carries
Characteristic image is taken, is weighted further according to the importance of each component using AHP algorithms, is finally entered using SVMs (SVM)
Row Classification and Identification.
The present invention compared with prior art, has the following advantages that by using above-mentioned technical proposal：
First, during admission identification, the data to be tested of collection in worksite and the gathered data in long-range client database will be invigilated
It is compared, compared with existing identification method, recognition efficiency and accuracy rate can be improved；Moreover, first critical region ownership place with
Can whether the territorial classification database where gathered data matches, and checking information is generated according to matching result, to judge obtain
Verifying authorization is obtained, and then knows whether data to be tested are verified by the admission on the region affiliation ground, different regions can be prevented
ID inquiring, checking between ownership place, improve the confidentiality of admission authentication.
2nd, the present invention is applied to invigilator scene, and when invigilator's identity is identified, identity is provided without personnel on site to be tested
These external things such as card, admission card for entrance examination, identification is carried out by human face image information, Consumer's Experience is good, and anti-counterfeiting performance is good, no
Easily forge and stolen；Examination face identification system will offer convenience to heavy business arrangement of examining, and improves admission efficiency, simplifies personnel
Set, there is preferably operability, reach the effect got twice the result with half the effort.
Brief description of the drawings
Fig. 1 is the chain graph of the system of the embodiment of the present invention.
Fig. 2 is the topological diagram of the system of the embodiment of the present invention.
Fig. 3 is each component of one layer of wavelet decomposition and its subgraph.
Fig. 4 is the basic thought schematic diagram of SVMs.
Fig. 5 is algorithm flow chart.
Fig. 6 is face experimental data base figure
Fig. 7 (I) algorithms of different result of the comparison comparison diagram.
Fig. 7 (II) algorithms of different result of the comparison comparison diagram.
Influence schematic diagram of Fig. 8 features accumulated value to discrimination
Embodiment
In conjunction with the drawings and specific embodiments, the present invention is further described.
As a specific embodiment, as depicted in figs. 1 and 2, a kind of new examination face authentication system of the invention
System, including data acquisition device, data processing equipment, cloud storage service device and multiple admissions checking harvester, data acquisition
Device is established data by wired or wireless internet and data processing equipment and communicated to connect, data processing equipment by wired or
WLAN establishes data communication connection with cloud storage service device,
Data acquisition device gathers character information and image information and generates gathered data, sends to data processing fill in real time
Put；Data processing equipment according to the region affiliation of gathered data, the remote port by acquired data storage to cloud storage service device
In database in corresponding territorial classification database；
Multiple admission checking harvester dispersed distributions are in regional ownership place admission check post, admission checking collection dress
Put collection Data Concurrent to be tested and deliver to cloud storage service device, cloud storage service device is compared the data to be tested with gathered data
It is right, judge the data to be tested region affiliation whether matched with the territorial classification database where corresponding gathered data,
Checking information is generated with result, to judge that can the data to be tested obtain verifying authorization, if so, then further passing through 1 ratio than N
Compared with method to knowing whether data to be tested match with gathered data, and then obtain whether the data to be tested are returned by the region
The admission checking in possession.
Another technical scheme of the present invention is a kind of new examination face authentication method, to comprise the following steps：
Data acquisition device gathers character information and image information and generates gathered data, sends to data processing fill in real time
Put；Data processing equipment according to the region affiliation of gathered data, the remote port by acquired data storage to cloud storage service device
In database in corresponding territorial classification database；
Multiple admission checking harvester dispersed distributions are in regional ownership place admission check post, field checking harvester
Gather Data Concurrent to be tested and deliver to cloud storage service device, cloud storage service device, which is adjusted, is compared the data to be tested with gathered data
It is right, judge the data to be tested region affiliation whether matched with the territorial classification database where corresponding gathered data,
Checking information is generated with result, to judge that can the data to be tested obtain verifying authorization, if so, then further passing through 1 ratio than N
Compared with method to knowing whether data to be tested match with gathered data, and then obtain whether the data to be tested are returned by the region
The admission checking in possession.
As shown in figure 1, being the chain graph of the system of the embodiment of the present invention, it is using directly upload, access, knowledge from bottom to top
Otherwise, i.e., user terminal directly can carry out real-time, interactive with the webserver.The system data acquisition device is integrated in
PC, flat board, mobile phone, the collection, storage, identification of face can be realized by data acquisition device；Data acquisition device uses hand
The modes such as machine card flow, wireless routing, broadband network are transmitted, and data processing equipment is then web-transporting device, the network transmission
Equipment has fire wall, load-equalizing switch (realizing that multiple terminal access is not delayed) etc., and Large Volume Data is effectively transmitted,
Prevent from distorting, attack.Cloud storage service device includes two-stage, and one-level is province, city site server, data progress is effectively stored,
Access, issue；Another Ji Wei great areas webserver, the data that each department upload are carried out with big data analysis, statistics examinee's letter
Breath, issues comprehensive examination district information in time.
The system allows user to carry out personalized customization, there is provided document content editor, edit model, reaches What You See Is What You Get
Effect.The system should have good security, scalability, can support more high traffic by hardware or software upgrading.
System using modularization, modularization (i.e. user can change recognizer), the design method of objectification, easy of integration, easy customization,
Possess good ability of second development, really make the investment of user minimum, the Maximum Value of creation.System provides daily management dimension
Shield, expansible, real-time, delay is small, recognition accuracy is high.
The system design meets the specification of national test office, suitable for different types of invigilator's certification, accurately identifies face
Information, high speed storing retrieval face resource, it is ensured that Information Security, there is provided the use habit of different crowd, network environment etc..
As shown in Fig. 2 being the topological diagram of the system, data center stores the human face data information in each province urban district, provinces and cities
Data center carries local face data message, and handheld terminal exactly directly compares the data message.
The characteristics of system is to use 1：N comparative approach, wherein N represent different N faces, and 1 representative needs to compare
Face.Traditional authentication method, it is to use 1:1 method, i.e., the locally downloading terminal of examination data of server end, so
The picture of download and the examination of admission are contrasted afterwards, examinee is judged whether according to comparison result.Such as examinee's A admissions
When, examinee A information has been downloaded in local terminal in advance, when examinee's A admissions, compares the photo downloaded, and I.Should
Significant deficiency be present in method because from the picture of the locally downloading terminal of server, can taking human as distort picture, thus can
Cause the mistake of authentication information, it is understood that there may be impersonate phenomenon.And the method proposed with the system, it can effectively evade such one
Kind of phenomenon, because N different pictures are stored in server end, local end user, the data of no weight update server end, than
To when, examinee need to compare the different pictures contrasts of N simultaneously.Such as after examinee A enters examination hall, handheld terminal login service
Device website, compare examinee A, if server end can find out examinee A information, can admission, be such as not present, be not then this
People.Conventional examination face authentication method, using 1：1 comparative approach, i.e., have server end under the face picture of test taker number
Local terminal is downloaded to, the test taker number of oneself is shown in now examinee's admission, can navigate to the people for having downloaded specific test taker number
Face photo, examinee and corresponding photo are then compared again, this method positioning is fast, it is fast to compare speed, is usually used in admission certification of taking an examination.
But the drawbacks of this method it is also obvious that i.e. invigilator person may change the local terminal photo downloaded, so will result in
Photo is changed, so that test-taker has an opportunity to take advantage of, therefore has more potential safety hazard using this method.What the embodiment proposed
1：N methods, effectively local information can be avoided to be tampered, i.e., examinee's admission when, invigilator person gathers examinee's photo, and after being directly connected to
Platform face database, is compared, and because backstage face database has N different examinee's face informations, therefore is 1 couple of N
Comparison, this method effectively prevent examinee information and is tampered, and avoids after examinee information is downloaded and compares again, has reached foreground
Collection, the effect of backstage high ratio pair.
The system has an advantage that, exactly transregional to set up different comparison data storehouses, the i.e. Verification System in somewhere only
This area can be logged in.Such as certain section examination hall of Quanzhou Licheng District, the database for logging in the section can only be corresponded to, so can
Effectively prevent that database access amount is big, the problem of comparison not in time.And the central server of Licheng District can check that the area owns
Examination hall authentication scenario under section, similarly Quanzhou City can check the examination authentication scenario in all areas.After the present embodiment proposes
Platform compares, and is that partition domain is compared, that is, which section the examinee gathered belongs to, just the examinee information ratio directly with the section
It is right, it is therefore prevented that the face information of big data compares, such as：Examinee A admissions Quanzhou the first examination district of Licheng District, invigilator person hold flat board
Instrument gathers examinee A, and now whole examinee informations of the examinee A only with the section compare, without doing ratio with citywide examinee information
It is right, avoid and compare repeatedly on a large scale, and the reduced time is shorter, can be applied to examination admission.
In the present embodiment, described 1 comparison method than N is specially：Cloud storage service device is by 1 data to be tested and N number of collection
Data are compared by the face recognition algorithms of characteristic weighing, and the face recognition algorithms of this feature weighting comprise the following steps：
Facial image is resolved into using wavelet transformation by high-low frequency weight first, principal component analysis (PCA) then is carried out to different components
Characteristic image is extracted, is weighted further according to the importance of each component using AHP algorithms, finally using SVMs (SVM)
Carry out Classification and Identification.
Wavelet transformation
Wavelet transformation is being developed rapidly nearly ten years, and it is extended out by Fourier transformation, using the teaching of the invention it is possible to provide multiresolution
And multiscale analysis, it has obtained application of result [4] in image processing and analyzing, computer vision, signal transacting etc..
Wavelet transformation was proposed for the first time in 1984 by researchers such as Morlet, if ψ (t) ∈ L2(R) it is one square
Integrable function, if ψ (t) meets following conditions：
Then ψ (t) is referred to as a wavelet function, and claims the admissible condition that formula (1) is wavelet function, stretches and translates
Wavelet mother function ψ (t), obtain wavelet basis function：
Wherein, a and τ is real number, and a>0, a is contraction-expansion factor, and τ is shift factor.
Function f (t) ∈ L2(R) continuous wavelet transform CWT is defined as follows：
In formulaFor the conjugate function of mother wavelet.A series of wavelet coefficient can obtain by formula (3), these are
Number is the function of shift factor and zoom factor.
In practical application, when handling small echo, it usually needs be discrete signal, now just need change the factor
A and continuous translation parameter τ size, it can not only so meet analysis of the signal on different scale, additionally it is possible to according to different
Purpose carrys out selecting scale.This analysis method is highly effective, as a result also very accurate.Wavelet transform is represented by：
Wherein, a0、b0For constant, and a0>0, m, n are integer.
The method taken herein is that a layer scattering wavelet transformation is carried out to facial image, generation horizontal component, vertical component
And diagonal components, as shown in figure 3, what is obtained is 4 subgraph components of face.
LL is low frequency component in Fig. 3, contains most information of artwork, and LH is the confidence of face, and HL is vertical
The marginal information such as component, nose, ear comprising people, HH is diagonal components, less comprising information.
2.2 principal component analysis methods
PCA (PCA) is a kind of conventional Mathematical Method, and it is the sample point certain correlation, choosing
The maximum direction of these sample point variances is taken to reconstitute one group of incoherent data, as feature space so as to compressed data
[3]。
If the size of facial image is m × n, by becoming the column vector of M=m × n dimensions, face training sample after vectorization
This is N, XiFor the column vector of i-th of sample, then training sample average value mu is taken：
After each training sample is subtracted face average again, matrix A=[X is formed1-μ,X2-μ,...,XN- μ], then
The covariance matrix of training sample is：
The optimal projection subspace to be found of characteristic vector composition corresponding to C nonzero eigenvalues is asked for again, in reality
Recognition of face in typically must accumulate contribution rate with characteristic valueTo determine the principal component dimension d to be chosen, general choosing
Taking makes characteristic vector construction feature space corresponding to the characteristic value of α >=90%.Then the matrix of feature space is U=[u1,u2,...,
ud], by training sample to projecting on feature space, obtain projection matrix：
Q=UTA (7)
The as eigenface of sample.
2.3AHP algorithm
Analytic hierarchy process (AHP) (AHP) be the U.S. plan strategies for scholar T.L.Saaty professor proposed in phase early 1970s,
AHP provides criteria decision-making method to a challenge.It needs to establish the structural model of Recurison order hierarchy, and construction judges square
Battle array, then Mode of Level Simple Sequence and consistency check [5] are carried out, the algorithm realizes that step is as follows：
1. problem stratification is constructed a structural model having levels first；
2. Judgement Matricies, the relation between hierarchical structure reflection factor, but each criterion institute's accounting in target measurement
Weight is different；
3. Mode of Level Simple Sequence and consistency check, determine the associated element importance of this level, and their order it
Between weighted value.
The key of AHP algorithms is Judgement Matricies, and it assigns different weights, drawn here according to the relation between factor
By the use of digital 1-9 and its inverse the implication of 1-9 scales is listed as scale, table 1.
The scoring criteria of element in the judgment matrix of table 1
Judged two-by-two by making n (n-1)/2 to each element in matrix, judgment matrix can be derived.
Maximum characteristic root λ corresponding to being asked for again to judgment matrix Amax：
A ω=λmaxω (9)
ω component is exactly the weighted value of the single sequence of corresponding factor.
Additionally need to carry out consistency check, order matrix B=(b to weighted valueij)n×n, wherein
Make againThus judgment matrix ordering vector w is obtainedi=(w1,w2,...wn)T's
Method is referred to as " and area method ".Now judgment matrix A eigenvalue of maximum can be approximated to beCalculate again consistent
Property index：
Finally calculate consistency rationWherein RI value is shown in Table 2.
The RI spans of table 2
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | 1.52 |
Total hierarchial sorting result is considered as CR ＜ 0.1 to be had more satisfied uniformity and receives the analysis result,
Otherwise just do not receive.Supplement is needed exist for, after consistency check is completed, also needs to examine order consistent judgment matrix
Property, it is finally reached crash consistency.
2.4 SVMs
SVMs grows up on the basis of Statistical Learning Theory, and it is a study mould for having supervision
Type, commonly used to carry out pattern-recognition, classification and regression analysis, its main thought be defined by interior Product function it is non-thread
Property become the input space of changing commanders and be mapped to a higher dimensional space, the data of original linearly inseparable is become the data of linear separability
[6], the optimal separating hyper plane of higher dimensional space is then solved again, as shown in Figure 4.
Suitable SVMs parameter is selected, face characteristic data label is extracted, the face characteristic data of extraction is entered
Row training obtains training set, then test sample is supplied to SVMs, and knowledge is provided by the supporting vector machine model trained
Other result.
3 algorithms are realized
Low frequency component was mainly extracted using wavelet transformation and PCA in the past and remove high fdrequency component, directly low
Using PCA algorithms extraction face characteristic on frequency component, then carry out svm classifier identification.The weak point of this method is directly to remove
The high-frequency information of face picture, it can make it that identification division is imperfect, each section of image all plays a role to identification,
Make full use of the useful information of each several part.
In view of above-mentioned analysis, set forth herein method be to take into full account facial image different piece, implementation step is as follows：
1. facial image is passed through into one layer of wavelet decomposition into 4 components of low-and high-frequency first；
2. and then different components are carried out with principal component analysis (PCA) extraction characteristic image；
3. it is weighted fusion using AHP algorithms further according to the importance of each component；
4. finally using the image after fusion as face characteristic, then all samples are divided into training set and test set, used
SVM carries out Classification and Identification, and its implementation process is as shown in Figure 5：
The weight calculation formula of the algorithm is as follows：
X=ω1LL+ω2LH+ω3HL+ω4HH (11)
Here 4 weights are according to the importance of different components, are calculated according to AHP algorithms, wherein ω1+ω2+ω3+
ω4=1.
4 experimental results and analysis
The algorithm is tested in classical face database, and the database has AT＆T, ORL, Yale etc..As shown in fig. 6,
It is the part face information of the database.
In experiment, the gray level image of face database is pre-processed first, picture format is unified for 112 × 92, every figure
Facial detail have difference.Then according to set forth herein algorithm, to image carry out wavelet transformation, generate four width PCA subgraphs,
Subgraph is synthesized according to AHP algorithms, then carries out svm classifier identification.
Experiment 1：In order to verify set forth herein algorithm, calculate recognition accuracy under different weights, research weights are to knowing
The not influence of rate.Selected in experiment per a kind of number of training N=5, α=90%, different sons are calculated further according to AHP algorithms
The weights of figure, carry out the experiment of multigroup weights, and experimental result is as follows：
Discrimination under this paper algorithm difference weights of table 3
As can be seen here, as the weights ω of low frequency part1During increase, when the weights of HFS are reduced, its recognition accuracy
Will increase.Therefore by setting image different piece weights, it is favorably improved the accuracy of recognition of face.
This paper algorithms are applied in experiment 2, and contrast principal component analyses the discrimination of method, verifies the accuracy of this paper algorithms.It is real every time
In testing, the corresponding weighted value of this paper algorithms, but different training sample number N is selected, every kind of 5 groups of algorithms selection enters respectively
OK, α=90% is taken every time, as table 4 shows：
The comparing result of the algorithms of different of table 4
(I)
(II)
It in weight is respectively 0.74,0.14,0.1,0.02 that table 4 (I), which is, calculates the discrimination that algorithms of different provides, (II)
It in weight is respectively that 0.65,0.15,0.17,0.03, Fig. 7 (I), (II) are corresponding tendency chart respectively to be, calculates algorithms of different
The discrimination provided.Can be seen that training sample is more, and accuracy rate is higher from result above, set forth herein algorithm than
Other two kinds of algorithm accuracys are high, consider to this paper differentiation the different piece of facial image.
The effect that contribution rate α is accumulated in PCA characteristic values is compared in experiment 3, and α value can influence the discrimination of algorithms of different,
Experimental comparison's PCA+SVM, 2DPCA+SNM, this paper algorithm (every kind of Algorithm for Training sample N=5, this paper algorithm weights take 0.74,
0.14,0.1,0.02), concrete outcome is as shown in Figure 8：
As can be seen from Figure 8, contribution rate α can influence the discrimination of algorithm, as α=95%, image recognition rate highest,
The main information of image all be used to identify, α takes can all cause discrimination to reduce during other values, therefore when carrying out PCA dimensionality reductions,
Main dimension selection and key into composition.
Although specifically showing and describing the present invention with reference to preferred embodiment, those skilled in the art should be bright
In vain, do not departing from the spirit and scope of the present invention that appended claims are limited, in the form and details can be right
The present invention makes a variety of changes, and is protection scope of the present invention.
Claims (4)
- A kind of 1. new examination face authentication system, it is characterised in that：Including data acquisition device, data processing equipment, cloud Storage server and multiple admissions checking harvester, data acquisition device are filled by wired or wireless internet and data processing Vertical data communication connection is set up, data processing equipment is established data with cloud storage service device by wired or wireless LAN and communicated Connection,Data acquisition device gathers character information and image information and generates gathered data, sends in real time to data processing equipment； Data processing equipment according to the region affiliation of gathered data, by the long-range end data of acquired data storage to cloud storage service device In storehouse in corresponding territorial classification database；Multiple admission checking harvester dispersed distributions are adopted in regional ownership place admission check post, admission checking harvester Collect Data Concurrent to be tested and deliver to cloud storage service device, the data to be tested are compared, sentenced by cloud storage service device with gathered data Whether matched with the territorial classification database where corresponding gathered data, matching result the region affiliation for the data to be tested of breaking Checking information is generated, to judge that can the data to be tested obtain verifying authorization, if so, then further passing through 1 comparison method pair than N Know whether the facial image of data to be tested matches with the facial image of gathered data, and then obtain whether the data to be tested pass through The admission checking on the region affiliation ground, wherein, 1 represents the facial image for needing to compare, and N represents N and compares face figure with this As relevant different facial images.
- A kind of 2. new examination face authentication system according to claim 1, it is characterised in that：Described 1 comparison than N Method is specially：Cloud storage service device adds the facial image of 1 data to be tested and the facial image of N number of gathered data by feature The face recognition algorithms of power are compared, and the face recognition algorithms of this feature weighting comprise the following steps：Become first using small echo Change and facial image is resolved into high-low frequency weight, then different components are carried out with principal component analysis extraction characteristic image, further according to The importance of each component is weighted using AHP algorithms, finally carries out Classification and Identification using SVMs.
- A kind of 3. new examination face authentication system according to claim 1, it is characterised in that：The data acquisition dress It is set to PC terminals, tablet terminal and/or mobile phone terminal.
- A kind of 4. new examination face authentication system according to claim 1, it is characterised in that：The cloud storage service Device includes two-stage, and one-level is province, city site server, and data are effectively stored, accesses, issue；Another Ji Wei great areas net Network server, big data analysis is carried out to the data that each department upload, examinee information is counted, issues comprehensive examination district information in time.
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| CN104866832B (en) * | 2015-05-29 | 2018-12-04 | 福建省智慧物联网研究院有限责任公司 | A kind of novel examination face authentication method |
| CN106846584A (en) * | 2017-02-13 | 2017-06-13 | 上海量明科技发展有限公司 | Shared bicycle and its unlocking method, lockset, terminal and system |
| CN107766796A (en) * | 2017-09-25 | 2018-03-06 | 郑州云海信息技术有限公司 | A kind of facial-recognition security systems and method based on cloud computing |
| CN108875514B (en) * | 2017-12-08 | 2021-07-30 | 北京旷视科技有限公司 | Face authentication method and system, authentication device and nonvolatile storage medium |
| CN108205834A (en) * | 2017-12-15 | 2018-06-26 | 深圳市商汤科技有限公司 | Access control management method and access control system |
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| CN101773394A (en) * | 2010-01-06 | 2010-07-14 | 中国航天员科研训练中心 | Identification method and identification system using identification method |
| CN103034880A (en) * | 2012-12-14 | 2013-04-10 | 上海第二工业大学 | Examination identity authentication system and identity authentication method based on face recognition |
| CN103208007A (en) * | 2013-03-19 | 2013-07-17 | 湖北微驾技术有限公司 | Face recognition method based on support vector machine and genetic algorithm |
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| JP4762805B2 (en) * | 2006-06-29 | 2011-08-31 | 富士通株式会社 | Biometric authentication method and biometric authentication system |
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| CN101773394A (en) * | 2010-01-06 | 2010-07-14 | 中国航天员科研训练中心 | Identification method and identification system using identification method |
| CN103034880A (en) * | 2012-12-14 | 2013-04-10 | 上海第二工业大学 | Examination identity authentication system and identity authentication method based on face recognition |
| CN103208007A (en) * | 2013-03-19 | 2013-07-17 | 湖北微驾技术有限公司 | Face recognition method based on support vector machine and genetic algorithm |
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