CN106940904A - Attendance checking system based on recognition of face and speech recognition - Google Patents
Attendance checking system based on recognition of face and speech recognition Download PDFInfo
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- CN106940904A CN106940904A CN201710151379.XA CN201710151379A CN106940904A CN 106940904 A CN106940904 A CN 106940904A CN 201710151379 A CN201710151379 A CN 201710151379A CN 106940904 A CN106940904 A CN 106940904A
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- 230000001815 facial effect Effects 0.000 claims abstract description 105
- 238000012545 processing Methods 0.000 claims abstract description 38
- 230000011218 segmentation Effects 0.000 claims description 28
- 238000000034 method Methods 0.000 claims description 12
- 230000003287 optical effect Effects 0.000 claims description 7
- 238000007873 sieving Methods 0.000 claims description 7
- 238000012805 post-processing Methods 0.000 claims description 5
- 230000009467 reduction Effects 0.000 claims description 4
- 235000013399 edible fruits Nutrition 0.000 claims description 2
- 230000008569 process Effects 0.000 claims description 2
- 238000011946 reduction process Methods 0.000 claims 1
- 238000012216 screening Methods 0.000 claims 1
- 230000009466 transformation Effects 0.000 description 6
- 238000006243 chemical reaction Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 238000001514 detection method Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000003709 image segmentation Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000001771 impaired effect Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000005204 segregation Methods 0.000 description 1
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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
- 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
- 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
-
- 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
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
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- Engineering & Computer Science (AREA)
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- Health & Medical Sciences (AREA)
- Human Computer Interaction (AREA)
- Multimedia (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Acoustics & Sound (AREA)
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Abstract
The invention provides the attendance checking system based on recognition of face and speech recognition, including facial image recognition processing module, voice recognition processing module and work attendance module, the facial image recognition processing module is used to obtain facial image, and processing identification is carried out to facial image, export facial image recognition result；The voice recognition processing module is used to carry out processing identification to typing voice and to voice, exports voice identification result；The work attendance module is used for when facial image recognition result and voice identification result are all identified, the attendance data storehouse that the typing of work attendance time is set.The present invention carries out work attendance otherwise using the knowledge based on voice and facial image, and the authenticity recognized to subject identity is high, and security is good, and work attendance is more accurate.
Description
Technical field
The present invention relates to human face detection tech field, and in particular to the attendance checking system based on recognition of face and speech recognition.
Background technology
Work attendance is identified by fingerprint in attendance checking system in correlation technique, when fingerprint is impaired, can directly affect work attendance
As a result, in addition, fingerprint is easily forged, the authenticity to the identification of subject identity is poor.
In correlation technique, human face image information is obtained by the way of face characteristic being identified processing.To face figure
As carrying out enhancing processing, edge and detailed information can be highlighted, while suppressing noise, improves the visual effect of facial image.Mesh
Before, wavelet transformation obtains certain effect in terms of image enhaucament, but wavelet transformation " optimal " can not represent to contain " line " or " face "
Unusual high-dimension function.Contourlet transformation is a kind of real two-dimensional image representation method, and the conversion is resolution more than one kind
, local, multidirectional image representing method.Change multiscale analysis of changing commanders separately is carried out with Orientation, can be preferably
Tiny directive profile and line segment are expressed, image enhancement processing can be performed well in.However, in the presence of contourlet transformation
Sampling, and it is in the absence of translation invariance, and artifact phenomenon can be produced in image after treatment.NSCT(Nonsubsampled
Contourlet transform, non-downsampling Contourlet conversion) be contourlet transformation a kind of improved procedure, should
Conversion eliminates the down-sampling link in contourlet transformation, and it has multiple dimensioned, multi-direction, locality and translation invariance
The features such as and be suitable in image enhancement technique.
In terms of image segmentation processing, there are a variety of extraordinary dividing methods, such as histogram thresholding is split
Method, iterative method Threshold segmentation and OTSU algorithms (maximum variance between clusters), these threshold segmentation methods can obtain very good
Segmentation effect, wherein OTSU algorithms can calculate the threshold value of input picture automatically, then with each pixel in input picture
It is compared, can be finally partitioned into target part and background parts in input picture, its arithmetic speed is than very fast.
The content of the invention
Regarding to the issue above, the present invention is intended to provide the attendance checking system based on recognition of face and speech recognition.
The purpose of the present invention is realized using following technical scheme：
Attendance checking system based on recognition of face and speech recognition, including at facial image recognition processing module, speech recognition
Manage module and work attendance module, the facial image recognition processing module is used to obtaining facial image, and to facial image at
Reason identification, exports facial image recognition result；The voice recognition processing module is used for typing voice and to voice
Reason identification, exports voice identification result；The work attendance module is used for
When identified, the attendance data storehouse that the typing of work attendance time is set.
Beneficial effects of the present invention are：Work attendance is carried out using the knowledge based on voice and facial image otherwise, to main body
The authenticity of identification is high, and security is good, and work attendance is more accurate.
Brief description of the drawings
Using accompanying drawing, the invention will be further described, but the embodiment in accompanying drawing does not constitute any limit to the present invention
System, for one of ordinary skill in the art, on the premise of not paying creative work, can also be obtained according to the following drawings
Other accompanying drawings.
The frame construction drawing of Fig. 1 present invention；
Fig. 2 is the frame construction drawing of the present inventor's face image recognition processing module.
Reference：
Facial image recognition processing module 1, voice recognition processing module 2, work attendance module 3, checking-in result display module 4,
Man face image acquiring unit 10, facial image filter element 11, facial image pretreatment unit 12, facial image post-processing unit
13rd, facial image recognition unit 14.
Embodiment
With reference to following application scenarios, the invention will be further described.
Referring to Fig. 1, the attendance checking system based on recognition of face and speech recognition of the present embodiment, including at facial image identification
Module 1, voice recognition processing module 2 and work attendance module 3 are managed, the facial image recognition processing module 1 is used to obtain face figure
Picture, and processing identification is carried out to facial image, export facial image recognition result；The voice recognition processing module 2 be used for pair
Typing voice simultaneously carries out processing identification to voice, exports voice identification result；The work attendance module 3 is used in facial image identification
When being all as a result identified with voice identification result, the attendance data storehouse that the typing of work attendance time is set.
Further, the attendance checking system based on recognition of face and speech recognition also includes checking-in result display module 4,
The checking-in result display module 4 passes through display screen when facial image recognition result and voice identification result are all identified
Work attendance success is shown, shows that work attendance is lost by display screen when facial image recognition result or voice identification result are recognition failures
Lose.
Preferably, the voice recognition processing module 2 carries out processing identification to voice, including：Typing voice is dropped
Make an uproar processing, the typing voice and the speech samples of speech database after noise reduction are compared one by one, according to the typing after noise reduction
The Distance Judgment of the speech samples of voice and speech database, if the distance is less than the threshold value of setting, exports speech recognition knot
Fruit is identified, and it is recognition failures otherwise to export voice identification result.
Preferably, referring to Fig. 2, the facial image recognition processing module 1 includes the man face image acquiring list being sequentially connected
Member 10, facial image filter element 11, facial image pretreatment unit 12, facial image post-processing unit 13, facial image are known
Other unit 14；The man face image acquiring unit 10 is used to obtain multiple facial images；The facial image filter element 11 is used
Effective facial image is screened in multiple facial images, others's face image is filtered；The facial image is located in advance
Reason unit 12 is used to tentatively pre-process the facial image filtered out, removes the random noise of facial image；The face
Post processing of image unit 13 is used to carry out dividing processing to facial image, obtains the face characteristic of facial image；The face figure
As recognition unit 14 is used to the face characteristic of facial image is identified.
The above embodiment of the present invention, work attendance is carried out using the knowledge based on voice and facial image otherwise, to main body body
The authenticity of part identification is high, and security is good, and work attendance is more accurate.
Preferably, the facial image filter element 11 screen facial image when according to customized optical sieving function
Screened, the facial image of value maximum of optical sieving function is chosen as preferred facial image, to remaining facial image
Deleted, wherein customized optical sieving function is：
In formula, W represents customized optical sieving function, ζiFor the average gray of the setting regions of i-th facial image
Value, ζ is the gray value threshold value set according to actual conditions, ηiFor the edge sharpness of i-th facial image, η is according to actual feelings
The edge sharpness threshold value of condition setting,For the quantity of facial image.
This preferred embodiment, sets facial image filter element 11, filters out optimal facial image and carries out facial image
Recognition detection, can greatly save system memory space, improve the speed and precision of facial image recognition detection.
Preferably, the random noise for removing facial image, including：
(1) NSCT conversion (non-downsampling Contourlet conversion) is carried out to the facial image filtered out, obtains the face
The low frequency sub-band coefficient and high-frequency sub-band coefficient of image；
(2) the high-frequency sub-band coefficient after decomposition is handled using anisotropic filter, improves the high-frequency sub-band after decomposing
Coefficient it is openness, then sampling is observed to high-frequency sub-band coefficient using pseudorandom Fourier matrix, obtains observation, for
Observation, is reconstructed using alternate segregation Bregman alternative manners, obtains optimal high-frequency sub-band coefficient；
(3) optimal high-frequency sub-band coefficient and the low frequency sub-band coefficient are subjected to Image Reconstruction together, that is, obtained after filtering
Facial image.
This preferred embodiment, denoising is carried out using aforesaid way to the facial image that filters out, being capable of clear comprehensive table
The marginal information for face image of leting others have a look at and its minutia, so as to realize effective image denoising, and are remained to greatest extent
The detailed information of facial image.
Preferably, it is described to facial image progress dividing processing, including：
(1) global segmentation threshold estimation is carried out to facial image using OTSU algorithms, obtains preferred global segmentation threshold value；
(2) whole facial image is divided into the multiple subgraphs of size identical；
(3) local segmentation threshold estimation is carried out to subgraph using OTSU algorithms, obtains the preferred part point of each subgraph
Cut threshold value；
(4) subgraph of diverse location is split using different segmentation thresholds, defines the segmentation threshold of subgraph
Computing formula be：
In formula, H is preferred global segmentation threshold value, HijThe preferred local segmentation threshold value of ' subgraph arranged for the i-th row jth, ρ
Represent the gray variance of whole facial image, ρijRepresent the gray variance of the subgraph of the i-th row jth row, δijRepresent the i-th row jth
The gray average of the subgraph of row, δ represents the gray average of whole facial image, min (λ1H,λ2Hij') represent from λ1H、λ2Hij′
Middle selection minimum value, λ1、λ2For the weight factor of setting, λ1+λ2=1；λkRepresent λ1H、λ2Hij' corresponding weight factor, when
λ1λ when H value is minimumk=λ1, work as λ2Hijλ during ' minimumk=λ2。
Wherein, it is described that global segmentation threshold estimation is carried out to facial image, be specially：The gray level of facial image is obtained,
And tonal range is determined according to the gray level, in the tonal range, select the initial segmentation threshold value of facial image；Traversal
The gray value of pixel in the facial image, chooses the pixel that the gray value is more than the initial segmentation threshold value, is used as prospect
Image, chooses the pixel that the gray value is less than the initial segmentation threshold value, as background image, calculates the foreground image
Gray average, the pixel count of the foreground image accounts for total pixel number purpose ratio, the gray average of the background image, the back of the body
The pixel count of scape image accounts for the gray average of total pixel number purpose ratio and the facial image；In the tonal range, increase
The segmentation threshold of the facial image, makes the difference value of foreground image and background image reach maximum, chooses foreground image and the back of the body
The segmentation threshold when difference value of scape image reaches maximum is as preferred global segmentation threshold value.
In this preferred embodiment, when splitting to image, each subgraph is divided using different segmentation thresholds
Cut, more closing to reality situation, more preferable segmentation effect can be realized, the segmentation threshold of its neutron image is by OSTU algorithms and son
The gray scale feature of image is together decided on, it is contemplated that the change of image local gray scale, can overcome by uneven illumination, interference of texture,
Facial image splits the influence caused the problems such as weak with background grey-scale contrast to facial image, so as to improve the essence of image segmentation
Degree, is easy to follow-up facial image to recognize so that attendance checking system is more accurate.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than to present invention guarantor
The limitation of scope is protected, although being explained with reference to preferred embodiment to the present invention, one of ordinary skill in the art should
Work as understanding, technical scheme can be modified or equivalent, without departing from the reality of technical solution of the present invention
Matter and scope.
Claims (6)
1. the attendance checking system based on recognition of face and speech recognition, it is characterized in that, including facial image recognition processing module, voice
Recognition processing module and work attendance module, the facial image recognition processing module are used to obtain facial image, and to facial image
Processing identification is carried out, facial image recognition result is exported；The voice recognition processing module is used for typing voice and to voice
Processing identification is carried out, voice identification result is exported；The work attendance module is used in facial image recognition result and speech recognition knot
When fruit is all identified, the attendance data storehouse that the typing of work attendance time is set.
2. the attendance checking system according to claim 1 based on recognition of face and speech recognition, it is characterized in that, also including work attendance
Result display module, the checking-in result display module is all identified in facial image recognition result and voice identification result
When, work attendance success is shown by display screen, when facial image recognition result or voice identification result are recognition failures by aobvious
Display screen shows work attendance failure.
3. the attendance checking system according to claim 2 based on recognition of face and speech recognition, it is characterized in that, the voice is known
Other processing module carries out processing identification to voice, including：To typing voice carry out noise reduction process, by the typing voice after noise reduction with
The speech samples of speech database are compared one by one, according to the typing voice after noise reduction and the speech samples of speech database
Distance Judgment, if the distance is less than the threshold value of setting, output voice identification result is identified, otherwise exports speech recognition
As a result it is recognition failures.
4. the attendance checking system according to claim 1 based on recognition of face and speech recognition, it is characterized in that, the face figure
As the man face image acquiring unit, facial image filter element, facial image that recognition processing module includes being sequentially connected are pre-processed
Unit, facial image post-processing unit, facial image recognition unit；The man face image acquiring unit is used to obtain multiple faces
Image；The facial image filter element is used to screen effective facial image in multiple facial images, to remaining face figure
As being filtered；The facial image pretreatment unit is used to tentatively pre-process the facial image filtered out, removes people
The random noise of face image；The facial image post-processing unit is used to carry out dividing processing to facial image, obtains face figure
The face characteristic of picture；The facial image recognition unit is used to the face characteristic of facial image is identified.
5. the attendance checking system according to claim 4 based on recognition of face and speech recognition, it is characterized in that, the face figure
As filter element is screened when screening facial image according to customized optical sieving function, optical sieving function is chosen
It is worth maximum facial image as preferred facial image, remaining facial image is deleted, wherein customized image is sieved
The function is selected to be：
In formula, W represents customized optical sieving function, ζiFor the average gray value of the setting regions of i-th facial image, ζ is
The gray value threshold value set according to actual conditions, ηiFor the edge sharpness of i-th facial image, η is to be set according to actual conditions
Edge sharpness threshold value,For the quantity of facial image.
6. the attendance checking system according to claim 5 based on recognition of face and speech recognition, it is characterized in that, it is described to face
Image carries out dividing processing, including：
(1) global segmentation threshold estimation is carried out to facial image using OTSU algorithms, obtains preferred global segmentation threshold value；
(2) whole facial image is divided into the multiple subgraphs of size identical；
(3) local segmentation threshold estimation is carried out to subgraph using OTSU algorithms, obtains the preferred local segmentation threshold of each subgraph
Value；
(4) subgraph of diverse location is split using different segmentation thresholds, defines the meter of the segmentation threshold of subgraph
Calculating formula is：
In formula, H is preferred global segmentation threshold value, HijThe preferred local segmentation threshold value of ' subgraph arranged for the i-th row jth, ρ is represented
The gray variance of whole facial image, ρijRepresent the gray variance of the subgraph of the i-th row jth row, δijRepresent the i-th row jth row
The gray average of subgraph, δ represents the gray average of whole facial image, min (λ1H,λ2Hij') represent from λ1H、λ2Hij' middle choosing
Select minimum value, λ1、λ2For the weight factor of setting, λ1+λ2=1；λkRepresent λ1H、λ2Hij' corresponding weight factor, works as λ1H's
It is worth λ during for minimumk=λ1, work as λ2Hijλ during ' minimumk=λ2。
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107633564A (en) * | 2017-08-31 | 2018-01-26 | 深圳市盛路物联通讯技术有限公司 | Monitoring method and Internet of Things server based on image |
| CN107958235A (en) * | 2017-12-28 | 2018-04-24 | 泰康保险集团股份有限公司 | A kind of facial image detection method, device, medium and electronic equipment |
| CN109870461A (en) * | 2019-03-29 | 2019-06-11 | 深圳市阿赛姆电子有限公司 | A kind of electronic component quality detection system |
| CN110012114A (en) * | 2019-05-05 | 2019-07-12 | 北京市众诚恒祥能源投资管理有限公司 | A kind of Environmental security early warning system based on Internet of Things |
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Denomination of invention: Work attendance system based on face identification and voice recognition Effective date of registration: 20200428 Granted publication date: 20190219 Pledgee: Bank of China Limited by Share Ltd. Guangzhou Panyu branch Pledgor: Networks Technology Co.,Ltd. Registration number: Y2020980001842 |
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Denomination of invention: Attendance system based on face recognition and speech recognition Effective date of registration: 20201016 Granted publication date: 20190219 Pledgee: Bank of China Limited by Share Ltd. Guangzhou Panyu branch Pledgor: Networks Technology Co.,Ltd. Registration number: Y2020980006879 |
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Denomination of invention: Attendance system based on face recognition and speech recognition Effective date of registration: 20210722 Granted publication date: 20190219 Pledgee: Bank of China Limited by Share Ltd. Guangzhou Panyu branch Pledgor: Networks Technology Co.,Ltd. Registration number: Y2021980006655 |
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Denomination of invention: Attendance system based on face recognition and speech recognition Effective date of registration: 20220601 Granted publication date: 20190219 Pledgee: Bank of China Limited by Share Ltd. Guangzhou Panyu branch Pledgor: Networks Technology Co.,Ltd. Registration number: Y2022980006892 |
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