CN109102612A - A kind of campus security management method and system - Google Patents
A kind of campus security management method and system Download PDFInfo
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- CN109102612A CN109102612A CN201811007793.4A CN201811007793A CN109102612A CN 109102612 A CN109102612 A CN 109102612A CN 201811007793 A CN201811007793 A CN 201811007793A CN 109102612 A CN109102612 A CN 109102612A
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- Prior art keywords
- image
- face
- security management
- condition code
- saturation
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- 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
- G07C9/30—Individual registration on entry or exit not involving the use of a pass
- G07C9/32—Individual registration on entry or exit not involving the use of a pass in combination with an identity check
- G07C9/37—Individual registration on entry or exit not involving the use of a pass in combination with an identity check using biometric data, e.g. fingerprints, iris scans or voice recognition
-
- 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
- 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
- G06V40/168—Feature extraction; Face representation
-
- 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 campus security management method and system, wherein campus security management method includes the following steps: the facial image of S1, the facial image for adding suspect, school teachers and students into face recognition database；The facial image of S2, continuously collector extract the condition code of facial image；S3, the condition code of extraction is compared with the condition code of the facial image of suspect in face recognition database, when the two is inconsistent, executes step S4, otherwise alarm, and at sending out notice message to administrative staff；S4, the condition code of extraction is compared with the condition code of the facial image of teachers and students in school in face recognition database, when the two is inconsistent, is considered as stranger, otherwise send opening signal to access control system.The present invention can carry out recognition of face to the personnel for being prepared to enter into campus, and register when finding stranger, find to alarm when suspect, advantageously ensure that the safety management in campus.
Description
Technical field
The present invention relates to campus security management technical field more particularly to a kind of campus security management method and system.
Background technique
Currently, domestic campus administration, mainly manually manages, since teachers and students in school's personnel amount is more, inevitably occur
Omission problem is arrived school once stranger even suspect enters, can be brought to the property of school and the person of teachers and students
Certain threat.In addition, during campus administration, there is also admit one's mistake people the case where.Therefore, in view of the above-mentioned problems, it is necessary to
It is proposed further solution.
Summary of the invention
It is existing in the prior art to overcome the purpose of the present invention is to provide a kind of campus security management method and system
It is insufficient.
For achieving the above object, the present invention provides a kind of campus security management method comprising following steps:
S1, the facial image for adding suspect add the facial image of teachers and students in school into face recognition database
Into face recognition database；
S2, identification monitoring area whether there is personnel, such as identify successfully, continuously the facial image of collector, extract
The condition code of facial image；
S3, the condition code of the facial image of suspect in the condition code of extraction and face recognition database is compared
It is right, when the two is inconsistent, step S4 is executed, is otherwise alarmed, and at sending out notice message to administrative staff；
S4, the condition code of the facial image of teachers and students in school in the condition code of extraction and face recognition database is compared
It is right, when the two is inconsistent, it is considered as stranger, otherwise sends opening signal to access control system.
As the improvement of campus security management method of the invention, described document information is position corresponding with human face five-sense-organ
Data and face mask data.
As the improvement of campus security management method of the invention, the campus security management method further includes to acquisition
Face image data is 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 campus security management 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 Campus security management system comprising: video camera, alarm
Device, access controller, administrative staff's user terminal and host, the video camera, administrative staff's user terminal and the host data
Transmission, the alarm, access controller and the host signal transmit；
The facial image of suspect and the people of teachers and students in school are stored in the face recognition database of the host
Face image；The video camera identifies the personnel in its monitoring area, and the continuously facial image of collector, described
The condition code of host extraction facial image；The host is by suspect in the condition code of extraction and face recognition database
The condition code of facial image is compared, and when the two is consistent, alarms, and sending out notice message is used to the administrative staff
Otherwise family end the condition code of extraction is compared with the condition code of the facial image of teachers and students in school in face recognition database,
When the two is consistent, opening signal is sent to the access controller, is otherwise considered as stranger.
As the improvement of Campus security management system of the invention, described document information is position corresponding with human face five-sense-organ
Data and face mask data.
As the improvement of Campus security management system of the invention, the Campus security management system is also used to acquisition
Face image data is 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 Campus security management system of the invention, the local energy spectrum gradient is counted as follows
It 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.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 can carry out the personnel for being prepared to enter into campus
Recognition of face, and registered when finding stranger, it finds to alarm when suspect, advantageously ensures that campus
Safety management, while having saved manpower, and the problem of avoid misidentification.
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 campus security management 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, campus security management method of the invention includes the following steps:
S1, the facial image for adding suspect add the facial image of teachers and students in school into face recognition database
Into face recognition database.
S2, identification monitoring area whether there is personnel, such as identify successfully, continuously the facial image of collector, extract
The condition code of facial image.
Wherein, described document information is position data corresponding with human face five-sense-organ and face mask data, is so passed through
The mode for extracting condition code, advantageously ensures that the accuracy of subsequent recognition of face.
S3, the condition code of the facial image of suspect in the condition code of extraction and face recognition database is compared
It is right, when the two is inconsistent, step S4 is executed, is otherwise alarmed, and at sending out notice message to administrative staff.
S4, the condition code of the facial image of teachers and students in school in the condition code of extraction and face recognition database is compared
It is right, when the two is inconsistent, it is considered as stranger, otherwise sends opening signal to access control system.
In addition, the campus security management 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 Campus security management systems comprising: video camera, report
Alert device, access controller, administrative staff's user terminal and host, the video camera, administrative staff's user terminal and the host number
According to transmission, the alarm, access controller and the host signal are transmitted.
Specifically, the facial image of suspect is stored in the face recognition database of the host and in school teacher
Raw facial image；The video camera identifies the personnel in its monitoring area, and the continuously face figure of collector
Picture, the host extract the condition code of facial image；The host is by crime in the condition code of extraction and face recognition database
The condition code of the facial image of suspect is compared, and when the two is consistent, alarms, and sending out notice message is to the pipe
Human user end is managed, otherwise, by the condition code of the facial image of teachers and students in school in the condition code of extraction and face recognition database
It is compared, when the two is consistent, sends opening signal to the access controller, be otherwise considered as stranger.Preferably, described
Condition code is position data corresponding with human face five-sense-organ and face mask data.
In addition, the Campus security management 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 can carry out recognition of face to the personnel for being prepared to enter into campus, and when finding stranger
It is registered, finds to alarm when suspect, advantageously ensure that the safety management in campus, while having saved manpower,
And the problem of avoiding misidentification.
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 campus security management method, which is characterized in that the campus security management method includes the following steps:
S1, the facial image for adding suspect add the facial image of teachers and students in school to people into face recognition database
In face identification database；
S2, identification monitoring area whether there is personnel, such as identify successfully, continuously the facial image of collector, extract face
The condition code of image；
S3, the condition code of extraction is compared with the condition code of the facial image of suspect in face recognition database,
When the two is inconsistent, step S4 is executed, is otherwise alarmed, and at sending out notice message to administrative staff；
S4, the condition code of extraction is compared with the condition code of the facial image of teachers and students in school in face recognition database, when
When the two is inconsistent, it is considered as stranger, otherwise sends opening signal to access control system.
2. campus security management method according to claim 1, which is characterized in that described document information be and human face five-sense-organ phase
Corresponding position data and face mask data.
3. campus security management method according to claim 1, which is characterized in that the campus security management method is also wrapped
It includes and processing is filtered to the face image data of acquisition:
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. campus security management method according to claim 3, which is characterized in that
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；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.
5. a kind of Campus security management system, which is characterized in that the Campus security management system include: video camera, alarm,
Access controller, administrative staff's user terminal and host, the video camera, administrative staff's user terminal and the host data pass
Defeated, the alarm, access controller and the host signal transmit；
The facial image of suspect and the face figure of teachers and students in school are stored in the face recognition database of the host
Picture；The video camera identifies the personnel in its monitoring area, and the continuously facial image of collector, the host
Extract the condition code of facial image；The host is by the face of suspect in the condition code of extraction and face recognition database
The condition code of image is compared, and when the two is consistent, alarms, and sending out notice message is to the administrative staff user
End, otherwise, the condition code of extraction is compared with the condition code of the facial image of teachers and students in school in face recognition database, when
When the two is consistent, opening signal is sent to the access controller, is otherwise considered as stranger.
6. Campus security management system according to claim 5, which is characterized in that described document information be and human face five-sense-organ phase
Corresponding position data and face mask data.
7. Campus security management system according to claim 5, which is characterized in that the Campus security management system is also used
In being filtered processing to the face image data of acquisition:
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. Campus security management system according to claim 7, which is characterized in that the local energy spectrum gradient is according to such as
Lower 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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| CN111145414A (en) * | 2019-12-27 | 2020-05-12 | 广东德融汇科技有限公司 | Campus safety management early warning method based on face recognition in K12 education stage |
| CN111462417A (en) * | 2020-03-09 | 2020-07-28 | 南京华科和鼎信息科技有限公司 | Multi-information verification system and multi-information verification method for unmanned bank |
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Application publication date: 20181228 |
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