CN109255851A - A kind of Work attendance method and system based on recognition of face - Google Patents
A kind of Work attendance method and system based on recognition of face Download PDFInfo
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- CN109255851A CN109255851A CN201811007563.8A CN201811007563A CN109255851A CN 109255851 A CN109255851 A CN 109255851A CN 201811007563 A CN201811007563 A CN 201811007563A CN 109255851 A CN109255851 A CN 109255851A
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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
- G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
- G07C1/10—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people together with the recording, indicating or registering of other data, e.g. of signs of identity
-
- 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
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
- G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
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- 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
- G06V40/168—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
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- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- Health & Medical Sciences (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Health & Medical Sciences (AREA)
- Human Computer Interaction (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
- Collating Specific Patterns (AREA)
Abstract
The present invention provides a kind of Work attendance method and system based on recognition of face, wherein the Work attendance method includes the following steps: the condition code for attendance personnel's face that S1, pre-read are stored in database profession；The face image data of S2, acquisition application enabling personnel, and extract condition code in face image data；S3, the condition code of extraction is compared with the condition code read in advance, when the threshold value of the two is inconsistent, does not record the disengaging information of personnel, and resurvey the face image data of application enabling personnel, otherwise, execute step S3；S4, the disengaging information for recording personnel, and send opening signal；S5, personnel's disengaging information of record is summarized, and is shown with patterned form.The present invention facilitates the attendance and management of personnel, recognition efficiency with higher and accuracy rate by the mode of recognition of face, facilitates attendance personnel and enters specified region, is conducive to personal management and improves the safety of institute control area.
Description
Technical field
The present invention relates to attendance technical field more particularly to a kind of Work attendance methods and system based on recognition of face.
Background technique
Attendance checking system refers to the management system of the attendance record equicorrelated case on and off duty of the employee of a set of management company.It is
The product of attendance software and attendance combination of hardware, generally HR department use, and grasp and manage employee attendance's dynamic of enterprise.So
And existing attendance checking system be based primarily upon swipe the card or the mode of fingerprint recognition to employee carry out attendance.Wherein, the mode swiped the card
There are problems that generation brush, and there are fingerprint recognition inaccuracy for the mode of fingerprint recognition, it is sometimes desirable to multiple fingerprint authentication could be complete
At staff attendance.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 Work attendance method and system based on recognition of face, to overcome in the prior art
Existing deficiency.
For achieving the above object, the present invention provides a kind of Work attendance method based on recognition of face comprising following step
It is rapid:
The condition code for attendance personnel's face that S1, pre-read are stored in database profession；
The face image data of S2, acquisition application enabling personnel, and extract condition code in face image data；
S3, the condition code of extraction is compared with the condition code read in advance, when the threshold value of the two is inconsistent, is not remembered
The disengaging information of record personnel, and the face image data of application enabling personnel is resurveyed, otherwise, execute step S3；
S4, the disengaging information for recording personnel, and send opening signal；
S5, personnel's disengaging information of record is summarized, and is shown with patterned form.
As the improvement of the Work attendance method of the invention based on recognition of face, the step S2 further includes the face to acquisition
Image data is filtered processing:
S21, the face image data according to acquisition calculate local energy spectrum gradient, histogram of gradients extension and maximum color
Degree saturation；
S22, it is saturated, is counted according to the local energy spectrum gradient, histogram of gradients extension and the maximum chrominance that are calculated
The ratio that pixel is obscured in entire image, effectively filters face image data.
As the improvement of the Work attendance method of the invention based on recognition of face, 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.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.
As the improvement of the Work attendance method of the invention based on recognition of face, the histogram of gradients extension is according to such as lower section
Method calculates:
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.
As the improvement of the Work attendance method of the invention based on recognition of face, the maximum chrominance saturation is as follows
It calculates:
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 attendance checking system based on recognition of face comprising: storage
Module, extraction module, face image data acquisition module, identification module, processing module；
The condition code of attendance personnel's face in memory module storing data library；
The condition code for attendance personnel's face that the extraction module pre-read is stored in database profession；
The face image data of the face image data acquisition module acquisition application enabling personnel, and extract facial image
Condition code in data；
The condition code of extraction is compared the identification mould with the condition code read in advance, when the threshold value of the two is inconsistent
When, the disengaging information of personnel is not recorded, and otherwise the face image data for resurveying application enabling personnel records personnel's
Information is passed in and out, and sends opening signal；
The processing module passes in and out information to the personnel of record and summarizes, and is shown with patterned form.
As the improvement of the attendance checking system of the invention based on recognition of face, the face image data acquisition module is also
Processing is filtered to the face image data of acquisition:
The face image data acquisition module calculates local energy and composes gradient, ladder according to the face image data of acquisition
Spend histogram extension and maximum chrominance saturation, and according to be calculated local energy spectrum gradient, histogram of gradients extension and most
Big coloration saturation, counts the ratio for obscuring pixel in entire image, is effectively filtered to face image data.
As the improvement of the attendance checking system of the invention based on recognition of face, the local energy spectrum gradient is according to as follows
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.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.
As the improvement of the attendance checking system of the invention based on recognition of face, the histogram of gradients extension is according to as follows
Method calculates:
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.
As the improvement of the attendance checking system of the invention based on recognition of face, the maximum chrominance saturation is according to such as lower section
Method calculates:
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 facilitates personnel by the mode of recognition of face
Attendance and management, recognition efficiency with higher and accuracy rate facilitate attendance personnel and enter specified region, favorably
In personal management and the safety of raising institute control area.
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 schematic diagram of the Work attendance method of the invention based on recognition of face.
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, the Work attendance method of the invention based on recognition of face includes the following steps:
The condition code for attendance personnel's face that S1, pre-read are stored in database profession；
The face image data of S2, acquisition application enabling personnel, and extract condition code in face image data；
S3, the condition code of extraction is compared with the condition code read in advance, when the threshold value of the two is inconsistent, is not remembered
The disengaging information of record personnel, and the face image data of application enabling personnel is resurveyed, otherwise, execute step S3；
S4, the disengaging information for recording personnel, and send opening signal；
S5, personnel's disengaging information of record is summarized, and is shown with patterned form.
In addition, the step S2 further includes being filtered processing to the face image data of acquisition:
S21, the face image data according to acquisition calculate local energy spectrum gradient, histogram of gradients extension and maximum color
Degree saturation；
S22, it is saturated, is counted according to the local energy spectrum gradient, histogram of gradients extension and the maximum chrominance that are calculated
The ratio that pixel is obscured in entire image, effectively filters face image data.
Specifically, 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 natural image model, the gradient distribution of fuzzy region is single, therefore σ1Value is very small, and clear area σ1Value compared with
Greatly.In one image with On Local Fuzzy, fuzzy and clear area topography's block will have Gaussian mixtures.
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.
With the above-mentioned Work attendance method based on recognition of face correspondingly, the present invention also provides a kind of based on recognition of face
Attendance checking system, which is characterized in that the attendance checking system includes: memory module, extraction module, face image data acquisition mould
Block, identification module, processing module.
Wherein, the condition code of attendance personnel's face in memory module storing data library；The extraction module is pre-read
Take the condition code for the attendance personnel's face being stored in database profession；The face image data acquisition module acquisition application enabling people
The face image data of member, and extract condition code in face image data；The identification mould is read by the condition code of extraction and in advance
The condition code taken is compared, and when the threshold value of the two is inconsistent, does not record the disengaging information of personnel, and resurvey application and open
Otherwise the face image data of door personnel records the disengaging information of personnel, and send opening signal；The processing module is to note
The personnel of record pass in and out information and summarize, and are shown with patterned form.
In addition, the face image data acquisition module is also filtered processing to the face image data of acquisition: described
Face image data acquisition module calculates local energy spectrum gradient, histogram of gradients extension according to the face image data of acquisition
It is saturated, and is saturated according to the local energy spectrum gradient, histogram of gradients extension and the maximum chrominance that are calculated, system with maximum chrominance
The ratio for obscuring pixel in entire image is counted out, face image data is effectively filtered.
Specifically, 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 natural image model, the gradient distribution of fuzzy region is single, therefore σ1Value is very small, and clear area σ1Value compared with
Greatly.In one image with On Local Fuzzy, fuzzy and clear area topography's block will have Gaussian mixtures.
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 mode of the present invention by recognition of face, facilitates the attendance and management of personnel, it is with higher
Recognition efficiency and accuracy rate facilitate attendance personnel and enter specified region, are conducive to personal management and improve controlled
The safety in region.
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 (10)
1. a kind of Work attendance method based on recognition of face, which is characterized in that the Work attendance method includes the following steps:
The condition code for attendance personnel's face that S1, pre-read are stored in database profession；
The face image data of S2, acquisition application enabling personnel, and extract condition code in face image data；
S3, the condition code of extraction is compared with the condition code read in advance, when the threshold value of the two is inconsistent, not recorder
The disengaging information of member, and the face image data of application enabling personnel is resurveyed, otherwise, execute step S3；
S4, the disengaging information for recording personnel, and send opening signal；
S5, personnel's disengaging information of record is summarized, and is shown with patterned form.
2. the Work attendance method according to claim 1 based on recognition of face, which is characterized in that the step S2 further includes pair
The face image data of acquisition is filtered processing:
It is full to calculate local energy spectrum gradient, histogram of gradients extension and maximum chrominance by S21, the face image data according to acquisition
With；
S22, it is saturated according to the local energy spectrum gradient, histogram of gradients extension and the maximum chrominance that are calculated, counts whole picture
The ratio that pixel is obscured in image, effectively filters face image data.
3. the Work attendance method according to claim 2 based on recognition of face, which is characterized in that the local energy composes gradient
It 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, 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.
4. the Work attendance method according to claim 2 based on recognition of face, which is characterized in that the histogram of gradients extension
It 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.
5. according to according to the Work attendance method as claimed in claim 2 based on recognition of face, which is characterized in that the maximum chrominance saturation
It 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.
6. a kind of attendance checking system based on recognition of face, which is characterized in that the attendance checking system includes: memory module, mentions
Modulus block, face image data acquisition module, identification module, processing module；
The condition code of attendance personnel's face in memory module storing data library；
The condition code for attendance personnel's face that the extraction module pre-read is stored in database profession；
The face image data of the face image data acquisition module acquisition application enabling personnel, and extract face image data
Middle condition code；
The condition code of extraction is compared the identification mould with the condition code read in advance, when the threshold value of the two is inconsistent,
The disengaging information of personnel is not recorded, and otherwise the face image data for resurveying application enabling personnel records the disengaging of personnel
Information, and send opening signal；
The processing module passes in and out information to the personnel of record and summarizes, and is shown with patterned form.
7. the attendance checking system according to claim 6 based on recognition of face, which is characterized in that the face image data
Acquisition module is also filtered processing to the face image data of acquisition:
It is straight to calculate local energy spectrum gradient, gradient according to the face image data of acquisition for the face image data acquisition module
Side's figure extension and maximum chrominance saturation, and according to local energy spectrum gradient, histogram of gradients extension and the maximum color being calculated
Degree saturation counts the ratio for obscuring pixel in entire image, is effectively filtered to face image data.
8. the attendance checking system according to claim 7 based on recognition of face, which is characterized in that the local energy spectrum ladder
Degree 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, 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.
9. the attendance checking system according to claim 7 based on recognition of face, which is characterized in that the histogram of gradients extension
It 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.
10. according to according to the attendance checking system as claimed in claim 7 based on recognition of face, which is characterized in that the maximum chrominance is full
It calculates as follows:
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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