CN104408780A - Face recognition attendance system - Google Patents
Face recognition attendance system Download PDFInfo
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- CN104408780A CN104408780A CN201410700067.6A CN201410700067A CN104408780A CN 104408780 A CN104408780 A CN 104408780A CN 201410700067 A CN201410700067 A CN 201410700067A CN 104408780 A CN104408780 A CN 104408780A
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- 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
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
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- 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
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/165—Detection; Localisation; Normalisation using facial parts and geometric relationships
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- 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
- G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
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- 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
- 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
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- Human Computer Interaction (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Geometry (AREA)
- Facsimile Image Signal Circuits (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a face recognition attendance system. The face recognition attendance system comprises a terminal database, an image preprocessor module, a face feature positioning module, an identity matching module, an image acquisition module, and a terminal display, wherein the image acquisition module is used for acquiring images and outputting image information to an image preprocessor; the image preprocessor is used for carrying out image processing such as light compensation and intensity normalization on the received images and then transmitting the processed image information to the face feature positioning module; the face feature positioning module is used for extracting face feature information and transmitting the processed information to the identity matching module; the identity matching module is used for reading face data from the terminal database and identifying the received image information and the face data; meanwhile, the identify matching module is used for storing the received image information and identification processing results in the terminal database. By virtue of the face recognition attendance system, the light influence can be reduced, the recognition rate can be increased, and the information management of the whole attendance system can be facilitated.
Description
Technical field
The present invention relates to electronic technology field, specifically a kind of human face identification work-attendance checking system of thing.
Background technology
At present, we work, conventional Time Attendance Device mainly contains and beats that paper card Time Attendance Device, IC-card Time Attendance Device, fingerprint attendance equipment and face are two identifies Time Attendance Device in life.Wherein, beat paper card Time Attendance Device and IC card Time Attendance Device separately need put papery or electronic attendance card, not only use cost is higher, also there is a subject matter: cannot stop generation and to check card phenomenon.Fingerprint attendance equipment affects by fingerprint pollution, impaired etc., and the limitation of use is larger; Meanwhile, because fingerprint attendance equipment is contact, the probability of communicate illness is increased greatly.
Now, market has also occurred some recognition of face Time Attendance Devices, and the discrimination of these human face identification work-attendance checking equipment is very large by the impact of light, is difficult to reach satisfied image effect, has recognition time long, refuses to recognize rate and the high shortcoming of misidentification rate.
Summary of the invention
The image that the object of the invention is by getting acquisition carries out suitable process and in conjunction with facial characteristic positioning method, the feature enabling it have shows in the picture significantly, thus the problem that the discrimination solving Time Attendance Device affects by light.
To achieve these goals, the invention provides following technical scheme:
A kind of human face identification work-attendance checking system, is characterized in that comprising:
Image capture module: for collector's facial information, obtain input picture;
Image pre-processor module, comprising:
Forward from rgb space the input picture collected to YCbCr color space, and carry out light compensation, the coloured image carried out after light compensation is converted into the device of black and white image;
Image after conversion is carried out the visual noise process reducing image, removes the smooth processing unit of the HFS in image simultaneously;
The multi-level gray scale image procossing gathering acquisition is become the binary conversion treatment device of bianry image;
Input picture is converted in each gray level, has identical pixel number, the grey level histogram of original image is become the equally distributed histogram equalization gasifying device in whole tonal range between certain gray area of relatively concentrating;
Facial Feature Localization module: by pretreated image according to the rule of rim detection orient respectively place between the eyebrows, two canthus, left and right, two pupils, two nostrils, face profile frontier point on two corners of the mouths and corners of the mouth horizontal line, and face feature information to be extracted;
Identities match module: the face feature information received is mated with the human face data in terminal database, and matching result is transferred to terminal display;
Terminal database: comprise human face data, employee's essential information and attendance record, the acquisition of human face data, after opening photo by camera to employee's facial information collection 5-10, preserves its characteristic of correspondence value by image procossing.
Preferably, the conversion formula of described gray scale normalization function is:
Wherein, c is the gray-scale value of original image, and G is the gray-scale value of image after conversion.This mapping algorithm is in [b, a] interval range the value transform of tonal range in [d, e] interval, the gray-scale value remained on outside [d, e] interval does not change, and the value of d is converted to b, the value of e is converted to a, a in formula, b, c, d, interval decimal integer value that e, G are [0,255].
Preferably, the conversion formula of described histogram equalization function is:
Wherein,
for grey scale pixel value,
for the grey scale pixel value after conversion,
for the area of image,
it is the number of pixels of i-th grade of gray scale.
Described image capture module is existing video camera product.
Described terminal display is LCD display.
The invention has the beneficial effects as follows by image pre-processing method is combined with facial Feature Localization method, solve the problem being easily subject to light impact utilizing the attendance checking system of visible ray to exist at present, add discrimination, and be convenient to the information management of whole attendance checking system.
Accompanying drawing explanation
Figure 1 shows that specific embodiments of the invention system chart.
Embodiment
For making content of the present invention more become apparent, further describe below in conjunction with the drawings and specific embodiments.
Shown in composition graphs 1, human face identification work-attendance checking system of the present invention comprises: terminal database, image pre-processor module, facial Feature Localization module, identities match module, image capture module, terminal display.Wherein:
Terminal database comprises human face data, employee's essential information and attendance record.The acquisition of human face data, after opening photo by camera to employee's facial information collection 5-10, preserves its characteristic of correspondence value by image procossing.This image processing method is by existing software simulating.
Image pre-processor module comprises:
The input picture collected is forwarded to YCbCr color space from rgb space, and carry out light compensation, coloured image after simultaneously carrying out light compensation is converted into the device of black and white image, because the picture that camera directly obtains may exist the unbalanced situation of light, the extraction to feature can be affected.
Coloured image after carrying out light compensation is converted into black and white image by smooth processing unit, so just the information of image more specifically, simply can be showed.The formula of conversion is:
Wherein, c is the gray-scale value of original image, and G is the gray-scale value of image after conversion.This mapping algorithm is in [b, a] interval range the value transform of tonal range in [d, e] interval, the gray-scale value remained on outside [d, e] interval does not change, and the value of d is converted to b, the value of e is converted to a, a in formula, b, c, d, interval decimal integer value that e, G are [0,255].
Image after conversion can be reduced the visual noise of image by binary conversion treatment device by smoothing processing, after removing the HFS in image, original unconspicuous low-frequency component can be made more easily to be identified simultaneously.Bianry image can be become by gathering the multi-level gray scale image procossing obtained, so that analysis and understanding and identification reduce calculated amount further by binary conversion treatment.Binaryzation changes the pixel color in image by a threshold value, makes in entire image picture and only has black and white two-value to be convenient to our extraction to feature.
Histogram equalization gasifying device makes input picture be converted to further by histogram equalization has identical pixel number in each gray level, the grey level histogram of original image is become being uniformly distributed in whole tonal range between certain gray area of relatively concentrating, and the formula of conversion is:
Wherein,
for grey scale pixel value,
for the grey scale pixel value after conversion,
for the area of image,
it is the number of pixels of i-th grade of gray scale.
Image pre-processing module by pretreated information transmission to facial Feature Localization module, by existing software by pretreated image according to the rule of rim detection orient respectively place between the eyebrows, two canthus, left and right, two pupils, two nostrils, face profile frontier point on two corners of the mouths and corners of the mouth horizontal line, and face feature information to be extracted.
After identities match module receives the image information after process, first from terminal database, human face data is read, and the image information received and human face data are contrasted, to carry out identifying processing to the image information received, and the result of process is sent to display; The simultaneously characteristic image information that will receive of identities match module, and identifying processing result is stored to terminal database, so that the extraction of data in the future.
Described in the present invention, concrete case study on implementation is only better case study on implementation of the present invention, is not used for limiting practical range of the present invention.Namely all equivalences done according to the content of the present patent application the scope of the claims change and modify, all should as technology category of the present invention.
Claims (4)
1. a human face identification work-attendance checking system, is characterized in that comprising:
Image capture module: for collector's facial information, obtain input picture;
Image pre-processor module, comprising:
Forward from rgb space the input picture collected to YCbCr color space, and carry out light compensation, the coloured image carried out after light compensation is converted into the device of black and white image;
Image after conversion is carried out the visual noise process reducing image, removes the smooth processing unit of the HFS in image simultaneously;
The multi-level gray scale image procossing gathering acquisition is become the binary conversion treatment device of bianry image;
Input picture is converted in each gray level, has identical pixel number, the grey level histogram of original image is become the equally distributed histogram equalization gasifying device in whole tonal range between certain gray area of relatively concentrating;
Facial Feature Localization module: by pretreated image according to the rule of rim detection orient respectively place between the eyebrows, two canthus, left and right, two pupils, two nostrils, face profile frontier point on two corners of the mouths and corners of the mouth horizontal line, and face feature information to be extracted;
Identities match module: the face feature information received is mated with the human face data in terminal database, and matching result is transferred to terminal display;
Terminal database: comprise human face data, employee's essential information and attendance record, the acquisition of human face data, after opening photo by camera to employee's facial information collection 5-10, preserves its characteristic of correspondence value by image procossing.
2., according to the human face identification work-attendance checking system described in claim 1, it is characterized in that the conversion formula of described gray scale normalization module is:
Wherein, c is the gray-scale value of original image, and G is the gray-scale value of image after conversion; This mapping algorithm is in [b, a] interval range the value transform of tonal range in [d, e] interval, the gray-scale value remained on outside [d, e] interval does not change, and the value of d is converted to b, the value of e is converted to a, a in formula, b, c, d, interval decimal integer value that e, G are [0,255].
3., according to the human face identification work-attendance checking system described in claim 1, it is characterized in that the conversion formula of histogram equalization module is:
Wherein,
for grey scale pixel value,
for the grey scale pixel value after conversion,
for the area of image,
it is the number of pixels of i-th grade of gray scale.
4., according to the human face identification work-attendance checking system described in claim 1, it is characterized in that terminal display is LCD display.
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| CN201410700067.6A CN104408780A (en) | 2014-11-28 | 2014-11-28 | Face recognition attendance system |
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104778544A (en) * | 2015-04-08 | 2015-07-15 | 葛续华 | Intelligent attendance system for student management |
| CN105684426A (en) * | 2016-01-19 | 2016-06-15 | 王晓光 | People counting method and system for video network meeting |
| CN105844727A (en) * | 2016-03-18 | 2016-08-10 | 中兴智能视觉大数据技术（湖北）有限公司 | Intelligent dynamic human face recognition attendance checking record management system |
| CN106023043A (en) * | 2016-06-25 | 2016-10-12 | 华北水利水电大学 | City planning citizen interaction system |
| CN107463875A (en) * | 2017-07-03 | 2017-12-12 | 金讯系统管理有限公司 | A kind of method and apparatus for judging personnel identity |
| CN108197615A (en) * | 2018-03-07 | 2018-06-22 | 北京上古视觉科技有限公司 | A kind of multimedium showing device and system |
| CN109255851A (en) * | 2018-08-31 | 2019-01-22 | 镇江赛唯思智能科技有限公司 | A kind of Work attendance method and system based on recognition of face |
| CN110532993A (en) * | 2019-09-04 | 2019-12-03 | 深圳市捷顺科技实业股份有限公司 | A kind of face method for anti-counterfeit, device, electronic equipment and medium |
| CN111027937A (en) * | 2019-12-10 | 2020-04-17 | 浩云科技股份有限公司 | Attendance system |
| CN111738742A (en) * | 2020-05-07 | 2020-10-02 | 广东电网有限责任公司 | Portrait data processing system for power customer service |
| CN116363736A (en) * | 2023-05-31 | 2023-06-30 | 山东农业工程学院 | Big data user information acquisition method based on digitalization |
| CN117765656A (en) * | 2024-02-21 | 2024-03-26 | 四川省肿瘤医院 | Control method and control system for gate of each ward of inpatient department |
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104778544A (en) * | 2015-04-08 | 2015-07-15 | 葛续华 | Intelligent attendance system for student management |
| CN105684426A (en) * | 2016-01-19 | 2016-06-15 | 王晓光 | People counting method and system for video network meeting |
| WO2017124295A1 (en) * | 2016-01-19 | 2017-07-27 | 王晓光 | Attendee counting method and system for network video conference |
| CN105844727A (en) * | 2016-03-18 | 2016-08-10 | 中兴智能视觉大数据技术（湖北）有限公司 | Intelligent dynamic human face recognition attendance checking record management system |
| CN106023043A (en) * | 2016-06-25 | 2016-10-12 | 华北水利水电大学 | City planning citizen interaction system |
| CN107463875A (en) * | 2017-07-03 | 2017-12-12 | 金讯系统管理有限公司 | A kind of method and apparatus for judging personnel identity |
| CN108197615A (en) * | 2018-03-07 | 2018-06-22 | 北京上古视觉科技有限公司 | A kind of multimedium showing device and system |
| CN109255851A (en) * | 2018-08-31 | 2019-01-22 | 镇江赛唯思智能科技有限公司 | A kind of Work attendance method and system based on recognition of face |
| CN110532993A (en) * | 2019-09-04 | 2019-12-03 | 深圳市捷顺科技实业股份有限公司 | A kind of face method for anti-counterfeit, device, electronic equipment and medium |
| CN111027937A (en) * | 2019-12-10 | 2020-04-17 | 浩云科技股份有限公司 | Attendance system |
| CN111738742A (en) * | 2020-05-07 | 2020-10-02 | 广东电网有限责任公司 | Portrait data processing system for power customer service |
| CN116363736A (en) * | 2023-05-31 | 2023-06-30 | 山东农业工程学院 | Big data user information acquisition method based on digitalization |
| CN116363736B (en) * | 2023-05-31 | 2023-08-18 | 山东农业工程学院 | Big data user information acquisition method based on digitalization |
| CN117765656A (en) * | 2024-02-21 | 2024-03-26 | 四川省肿瘤医院 | Control method and control system for gate of each ward of inpatient department |
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Application publication date: 20150311 |