CN109102611A - A kind of identity checking method and system - Google Patents
A kind of identity checking method and system Download PDFInfo
- Publication number
- CN109102611A CN109102611A CN201811007600.5A CN201811007600A CN109102611A CN 109102611 A CN109102611 A CN 109102611A CN 201811007600 A CN201811007600 A CN 201811007600A CN 109102611 A CN109102611 A CN 109102611A
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- CN
- China
- Prior art keywords
- identity
- personnel
- image
- saturation
- feature code
- Prior art date
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Classifications
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C9/00—Individual registration on entry or exit
- G07C9/20—Individual registration on entry or exit involving the use of a pass
- G07C9/22—Individual registration on entry or exit involving the use of a pass in combination with an identity check of the pass holder
- G07C9/25—Individual registration on entry or exit involving the use of a pass in combination with an identity check of the pass holder using biometric data, e.g. fingerprints, iris scans or voice recognition
- G07C9/253—Individual registration on entry or exit involving the use of a pass in combination with an identity check of the pass holder using biometric data, e.g. fingerprints, iris scans or voice recognition visually
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
- G06T7/41—Analysis of texture based on statistical description of texture
- G06T7/42—Analysis of texture based on statistical description of texture using transform domain methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
-
- 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
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
Abstract
The present invention provides a kind of identity checking method and system, wherein the identity checking method includes the following steps: S1, reads personnel identity certificate information, and identify personnel's photo on identity document, when identifying the fisrt feature code for successfully extracting facial image, otherwise re-recognize；S2, it recognizes whether to need to veritify the personnel of identity, such as identifies successfully, continuously the human face data of collector, and extract the second feature code of personnel's facial image, otherwise re-recognize；S3, the fisrt feature code and second feature code are compared, when the two is consistent, export on-off model, starts gate, and store comparison data.The present invention improves the accuracy to personnel identity identification, facilitates qualified user and enter the region that access control system is controlled, and improve the region security that access control system is controlled by carrying out dual identification to human face photo on face and identity document.
Description
Technical field
The present invention relates to technical field of face recognition more particularly to a kind of identity checking method and systems.
Background technique
Access control system is new-modernization safety management system, collects microcomputer automatic identification technology and modern safety management is arranged
It applies and is integrated.Access control system has surmounted simple gateway and key management already, has evolved into as complete set
Access management system.Simultaneously access control system played in the Administrations such as work circumstances safe, personnel attendance management compared with
Big effect.For existing access control system mainly using mode of swiping the card, the card that other staff still can use user passes through door
Access control system.Therefore, in view of the above-mentioned problems, it is necessary to propose further solution.
Summary of the invention
The purpose of the present invention is to provide a kind of identity checking method and system, with overcome it is existing in the prior art not
Foot.
For achieving the above object, the present invention provides a kind of identity checking method comprising following steps:
S1, personnel identity certificate information is read, and identifies personnel's photo on identity document, when identifying successfully, extract people
The fisrt feature code of face image, otherwise re-recognizes；
S2, it recognizes whether to need to veritify the personnel of identity, such as identify successfully, continuously the face number of collector
According to, and the second feature code of personnel's facial image is extracted, otherwise re-recognize；
S3, the fisrt feature code and second feature code are compared, when the two is consistent, export on-off model,
Start gate, and stores comparison data.
As the improvement of identity checking method of the invention, the personnel identity certificate information includes: name, address, nationality
It passes through, gender, date of birth.
As the improvement of identity checking method of the invention, the identity checking method further includes the facial image to acquisition
Data are filtered processing:
According to the face image data of acquisition, it is full to calculate local energy spectrum gradient, histogram of gradients extension and maximum chrominance
With；
According to local energy spectrum gradient, histogram of gradients extension and the maximum chrominance saturation being calculated, whole picture is counted
The ratio that pixel is obscured in image, effectively filters face image data.
As the improvement of identity checking method of the invention, the local energy spectrum gradient calculates as follows:
The energy spectrum of NxN sized images is first calculated with discrete Fourier transform:
Then it converts to polar coordinates u=fcos θ, v=fsin θ, and calculates S (f, θ), obtain:
Wherein, A is the amplitude factor in an all directions, and α is energy spectrum slope.Largely studies have shown that scheming naturally
α is about 2 as in, and fuzzy image has biggish α.Therefore the On Local Fuzzy degree of image can be described as part and global-alpha value
Proportional difference
Wherein, αpIt is local α, αoIt is global-alpha；
The histogram of gradients extension calculates as follows:
The gradient of each pixel of image is first calculated, then with containing there are two the ladders of the gauss hybrid models of Gauss description part
Degree distribution: π0G(x；μ0, σ0)+π1G(x；μ1, σ1), wherein σ1>σ0；
According to gradient distribution, the specific formula for calculation of histogram of gradients extension is
Wherein, CpIt is topography's intensity value ranges, ε is the minimum number prevented except zero, and τ is a constant, takes 25；
The maximum chrominance saturation calculates as follows:
First calculate the saturation degree of each pixel:
Then compare local saturation maximum value and global saturation degree maximum value using following formula, it is full to obtain maximum chrominance
With:
Wherein, max (sp) it is saturation degree maximum value in topography's block, max (so) it is that saturation degree is maximum in global image
Value.
For achieving the above object, the present invention provides a kind of identity verifying system comprising: camera, card reader with
And host, the host are connected with the camera and card reader, and receive the data of the camera and card reader transmission
Information；
The card reader identifies personnel's photo on identity document for reading personnel identity certificate information, works as identification
Success, the host extract the fisrt feature code of facial image；
The camera whether there is the personnel for needing to veritify identity for identification, such as identifies successfully, continuously acquires people
The facial image of member, the host extract the second feature code of personnel's image；
The fisrt feature code and second feature code are compared the host, when the two is consistent, export switching value
Signal starts gate, and stores comparison data.
As the improvement of identity verifying system of the invention, the personnel identity certificate information includes: name, address, nationality
It passes through, gender, date of birth.
As the improvement of identity verifying system of the invention, the identity verifying system is also used to the facial image to acquisition
Data are filtered processing:
According to the face image data of acquisition, it is full to calculate local energy spectrum gradient, histogram of gradients extension and maximum chrominance
With；
According to local energy spectrum gradient, histogram of gradients extension and the maximum chrominance saturation being calculated, whole picture is counted
The ratio that pixel is obscured in image, effectively filters face image data.
As the improvement of identity verifying system of the invention, the local energy spectrum gradient calculates as follows:
The energy spectrum of NxN sized images is first calculated with discrete Fourier transform:
Then it converts to polar coordinates u=fcos θ, v=fsin θ, and calculates S (f, θ), obtain:
Wherein, A is the amplitude factor in an all directions, and α is energy spectrum slope.Largely studies have shown that scheming naturally
α is about 2 as in, and fuzzy image has biggish α.Therefore the On Local Fuzzy degree of image can be described as part and global-alpha value
Proportional difference
Wherein, αpIt is local α, αoIt is global-alpha；
The histogram of gradients extension calculates as follows:
The gradient of each pixel of image is first calculated, then with containing there are two the ladders of the gauss hybrid models of Gauss description part
Degree distribution: π0G(x；μ0, σ0)+π1G(x；μ1, σ1), wherein σ1>σ0；
According to gradient distribution, the specific formula for calculation of histogram of gradients extension is
Wherein, CpIt is topography's intensity value ranges, ε is the minimum number prevented except zero, and τ is a constant, takes 25；
The maximum chrominance saturation calculates as follows:
First calculate the saturation degree of each pixel:
Then compare local saturation maximum value and global saturation degree maximum value using following formula, it is full to obtain maximum chrominance
With:
Wherein, max (sp) it is saturation degree maximum value in topography's block, max (so) it is that saturation degree is maximum in global image
Value.
Compared with prior art, the beneficial effects of the present invention are: the present invention passes through to face on face and identity document
Photo carries out dual identification, improves the accuracy to personnel identity identification, facilitates qualified user and enters gate inhibition system
The controlled region of system, and improve the region security that access control system is controlled.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The some embodiments recorded in invention, for those of ordinary skill in the art, without creative efforts,
It is also possible to obtain other drawings based on these drawings.
Fig. 1 is the method flow schematic diagram of a specific embodiment of identity checking method of the invention.
Specific embodiment
The present invention is described in detail for each embodiment shown in reference to the accompanying drawing, but it should be stated that, these
Embodiment is not limitation of the present invention, those of ordinary skill in the art according to these embodiments made by function, method,
Or equivalent transformation or substitution in structure, all belong to the scope of protection of the present invention within.
As shown in Figure 1, identity checking method of the invention includes the following steps:
S1, personnel identity certificate information is read, and identifies personnel's photo on identity document, when identifying successfully, extract people
The fisrt feature code of face image, otherwise re-recognizes.
Wherein, personnel identity certificate information is personnel identity card.To which the personnel identity certificate information includes: surname
Name, address, native place, gender, date of birth.The fisrt feature code is that position data corresponding with human face five-sense-organ and face take turns
Wide data.
S2, it recognizes whether to need to veritify the personnel of identity, such as identify successfully, continuously the face number of collector
According to, and the second feature code of personnel's facial image is extracted, otherwise re-recognize.
Wherein, the second feature code is position data corresponding with human face five-sense-organ and face mask data.
S3, the fisrt feature code and second feature code are compared, when the two is consistent, export on-off model,
Start gate, and stores comparison data.
In addition, the identity checking method further includes being filtered processing to the face image data of acquisition:
According to the face image data of acquisition, it is full to calculate local energy spectrum gradient, histogram of gradients extension and maximum chrominance
With；
According to local energy spectrum gradient, histogram of gradients extension and the maximum chrominance saturation being calculated, whole picture is counted
The ratio that pixel is obscured in image, effectively filters face image data.
Wherein, the local energy spectrum gradient calculates as follows:
The energy spectrum of NxN sized images is first calculated with discrete Fourier transform:
Then it converts to polar coordinates u=fcos θ, v=fsin θ, and calculates S (f, θ), obtain:
Wherein, A is the amplitude factor in an all directions, and α is energy spectrum slope.Largely studies have shown that scheming naturally
α is about 2 as in, and fuzzy image has biggish α.Therefore the On Local Fuzzy degree of image can be described as part and global-alpha value
Proportional difference
Wherein, αpIt is local α, αoIt is global-alpha；
The histogram of gradients extension calculates as follows:
The gradient of each pixel of image is first calculated, then with containing there are two the ladders of the gauss hybrid models of Gauss description part
Degree distribution: π0G(x；μ0, σ0)+π1G(x；μ1, σ1), wherein σ1>σ0；
According to gradient distribution, the specific formula for calculation of histogram of gradients extension is
Wherein, CpIt is topography's intensity value ranges, ε is the minimum number prevented except zero, and τ is a constant, takes 25；
The maximum chrominance saturation calculates as follows:
First calculate the saturation degree of each pixel:
Then compare local saturation maximum value and global saturation degree maximum value using following formula, it is full to obtain maximum chrominance
With:
Wherein, max (sp) it is saturation degree maximum value in topography's block, max (so) it is that saturation degree is maximum in global image
Value.
Based on identical inventive concept, the present invention also provides a kind of identity verifying systems comprising: camera, card reader
And host, the host are connected with the camera and card reader, and receive the number of the camera and card reader transmission
It is believed that breath.
Wherein, the card reader is for reading personnel identity certificate information, and identifies personnel's photo on identity document, when
It identifies successfully, the host extracts the fisrt feature code of facial image.The camera whether there is for identification to be needed to veritify
The personnel of identity such as identify successfully, continuously the facial image of collector that the host extracts the second feature of personnel's image
Code.The fisrt feature code and second feature code are compared the host, when the two is consistent, export on-off model,
Start gate, and stores comparison data.
Preferably, the personnel identity certificate information includes: name, address, native place, gender, date of birth.Described first is special
Levy code, second feature code is position data corresponding with human face five-sense-organ and face mask data.
In addition, the identity verifying system is also used to be filtered processing to the face image data of acquisition:
According to the face image data of acquisition, it is full to calculate local energy spectrum gradient, histogram of gradients extension and maximum chrominance
With；
According to local energy spectrum gradient, histogram of gradients extension and the maximum chrominance saturation being calculated, whole picture is counted
The ratio that pixel is obscured in image, effectively filters face image data.
Wherein, the local energy spectrum gradient calculates as follows:
The energy spectrum of NxN sized images is first calculated with discrete Fourier transform:
Then it converts to polar coordinates u=fcos θ, v=fsin θ, and calculates S (f, θ), obtain:
Wherein, A is the amplitude factor in an all directions, and α is energy spectrum slope.Largely studies have shown that scheming naturally
α is about 2 as in, and fuzzy image has biggish α.Therefore the On Local Fuzzy degree of image can be described as part and global-alpha value
Proportional difference
Wherein, αpIt is local α, αoIt is global-alpha；
The histogram of gradients extension calculates as follows:
The gradient of each pixel of image is first calculated, then with containing there are two the ladders of the gauss hybrid models of Gauss description part
Degree distribution: π0G(x；μ0, σ0)+π1G(x；μ1, σ1), wherein σ1>σ0；
According to gradient distribution, the specific formula for calculation of histogram of gradients extension is
Wherein, CpIt is topography's intensity value ranges, ε is the minimum number prevented except zero, and τ is a constant, takes 25；
The maximum chrominance saturation calculates as follows:
First calculate the saturation degree of each pixel:
Then compare local saturation maximum value and global saturation degree maximum value using following formula, it is full to obtain maximum chrominance
With:
Wherein, max (sp) it is saturation degree maximum value in topography's block, max (so) it is that saturation degree is maximum in global image
Value.
In conclusion the present invention by carrying out dual identification to human face photo on face and identity document, improves pair
The accuracy of personnel identity identification, facilitates qualified user and enters the region that access control system is controlled, and improve door
The region security that access control system is controlled.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiments being understood that.
Claims (8)
1. a kind of identity checking method, which is characterized in that the identity checking method includes the following steps:
S1, personnel identity certificate information is read, and identifies personnel's photo on identity document, when identifying successfully, extract face figure
The fisrt feature code of picture, otherwise re-recognizes；
S2, it recognizes whether to need to veritify the personnel of identity, such as identify successfully, continuously the human face data of collector, and
The second feature code of extraction personnel's facial image, otherwise re-recognizes；
S3, the fisrt feature code and second feature code are compared, when the two is consistent, export on-off model, starting
Gate, and store comparison data.
2. identity checking method according to claim 1, which is characterized in that the personnel identity certificate information includes: surname
Name, address, native place, gender, date of birth.
3. identity checking method according to claim 1, which is characterized in that the identity checking method further includes to acquisition
Face image data be filtered processing:
According to the face image data of acquisition, local energy spectrum gradient, histogram of gradients extension and maximum chrominance saturation are calculated；
According to local energy spectrum gradient, histogram of gradients extension and the maximum chrominance saturation being calculated, entire image is counted
In obscure pixel ratio, face image data is effectively filtered.
4. identity checking method according to claim 1, which is characterized in that the local energy spectrum gradient is according to such as lower section
Method calculates:
The energy spectrum of NxN sized images is first calculated with discrete Fourier transform:
Then it converts to polar coordinates u=fcos θ, v=fsin θ, and calculates S (f, θ), obtain:
Wherein, A is the amplitude factor in an all directions, and α is energy spectrum slope；It is a large amount of studies have shown that α in natural image
About 2, fuzzy image has biggish α.Therefore the On Local Fuzzy degree of image can be described as the ratio of part and global-alpha value
Difference
Wherein, αpIt is local α, αoIt is global-alpha；
The histogram of gradients extension calculates as follows:
The gradient for first calculating each pixel of image, is then retouched with containing the gauss hybrid models there are two Gauss
State local gradient distribution: π0G(x；μ0, σ0)+π1G(x；μ1, σ1), wherein σ1>σ0；
According to gradient distribution, the specific formula for calculation of histogram of gradients extension is
Wherein, CpIt is topography's intensity value ranges, ε is the minimum number prevented except zero, and τ is a constant, takes 25；
The maximum chrominance saturation calculates as follows:
First calculate the saturation degree of each pixel:
Then compare local saturation maximum value and global saturation degree maximum value using following formula, obtain maximum chrominance saturation:
Wherein, max (sp) it is saturation degree maximum value in topography's block, max (so) it is saturation degree maximum value in global image.
5. a kind of identity verifying system, which is characterized in that the identity verifying system includes: camera, card reader and host,
The host is connected with the camera and card reader, and receives the data information of the camera and card reader transmission；
The card reader identifies personnel's photo on identity document for reading personnel identity certificate information, when identifying successfully,
The host extracts the fisrt feature code of facial image；
The camera whether there is the personnel for needing to veritify identity for identification, such as identify successfully, continuously collector
Facial image, the host extract the second feature code of personnel's image；
The fisrt feature code and second feature code are compared the host, when the two is consistent, export on-off model,
Start gate, and stores comparison data.
6. identity verifying system according to claim 1, which is characterized in that the personnel identity certificate information includes: surname
Name, address, native place, gender, date of birth.
7. identity verifying system according to claim 1, which is characterized in that the identity verifying system is also used to acquisition
Face image data be filtered processing:
According to the face image data of acquisition, local energy spectrum gradient, histogram of gradients extension and maximum chrominance saturation are calculated；
According to local energy spectrum gradient, histogram of gradients extension and the maximum chrominance saturation being calculated, entire image is counted
In obscure pixel ratio, face image data is effectively filtered.
8. identity verifying system according to claim 1, which is characterized in that the local energy spectrum gradient is according to such as lower section
Method calculates:
The energy spectrum of NxN sized images is first calculated with discrete Fourier transform:
Then it converts to polar coordinates u=fcos θ, v=fsin θ, and calculates S (f, θ), obtain:
Wherein, A is the amplitude factor in an all directions, and α is energy spectrum slope；It is a large amount of studies have shown that α in natural image
About 2, fuzzy image has biggish α.Therefore the On Local Fuzzy degree of image can be described as the ratio of part and global-alpha value
Difference
Wherein, αpIt is local α, αoIt is global-alpha；
The histogram of gradients extension calculates as follows:
The gradient of each pixel of image is first calculated, then with containing there are two the gradients point of the gauss hybrid models of Gauss description part
Cloth: π0G(x；μ0, σ0)+π1G(x；μ1, σ1), wherein σ1>σ0；
According to gradient distribution, the specific formula for calculation of histogram of gradients extension is
Wherein, CpIt is topography's intensity value ranges, ε is the minimum number prevented except zero, and τ is a constant, takes 25；
The maximum chrominance saturation calculates as follows:
First calculate the saturation degree of each pixel:
Then compare local saturation maximum value and global saturation degree maximum value using following formula, obtain maximum chrominance saturation:
Wherein, max (sp) it is saturation degree maximum value in topography's block, max (so) it is saturation degree maximum value in global image.
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Application publication date: 20181228 |
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| RJ01 | Rejection of invention patent application after publication |