CN201374072Y - Processor based on face recognition and living body recognition - Google Patents
Processor based on face recognition and living body recognition Download PDFInfo
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- CN201374072Y CN201374072Y CN200820214488U CN200820214488U CN201374072Y CN 201374072 Y CN201374072 Y CN 201374072Y CN 200820214488 U CN200820214488 U CN 200820214488U CN 200820214488 U CN200820214488 U CN 200820214488U CN 201374072 Y CN201374072 Y CN 201374072Y
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Abstract
The utility model relates to a recognition device, in particular to a processor based on face recognition and living body recognition. The utility model has the technical scheme that the processor based on the face recognition and the living body recognition comprises a dicaryotic chip SCO module, a living body recognition module and a server, wherein the dicaryotic chip SCO module is connected on the output end of a network interface module; the network interface module is used for receiving videos or images from the network; the living body recognition module is used for recognizing the sign of life of the detected people, and is connected with the output end of the dicaryotic chip SCO module; and then, the server which is used for storing information is connected with the output end of the living body recognition module. The utility model can solve the issuing problem of the social security money or endowment insurance money of retired people, and can simultaneously and greatly save the cost.
Description
Technical field
The utility model relates to recognition device, specifically a kind for the treatment of apparatus based on recognition of face and vivo identification.
Technical background
Along with advancing of China's reform and opening-up paces, Labour ﹠ Social Security's cause has also had the development of advancing by leaps and bounds.Along with the reform of social security system, the national socialized delivery of pension is also constantly perfect.
Assurance is granted pension through social channel accurately; a main prerequisite that is in time is exactly to want accurately to confirm insurer's survival condition; and domestic social security mechanism for a long time not effective means handle this problem, so endowment insurance money false claiming problem ubiquity.
According to the relevant data statistics, present retired personnel has 18% people to live in the other places, such as, help sons and daughters to childmind in the other places, settle down or the like abroad.If government provides social security gold or endowment insurance money to them, they must return to lead in person, and so cost is too big concerning those retired personnels.If do not get in person, that social security department can not confirm this granting is the retired personnel.If the retired personnel is dead, because in the other places, these personnel's sign of life, the granting that causes social security gold or endowment insurance money not to stop can't be known in local social security department.
Whether also to provide the social security gold for these retired personnel governments to them? how they are provided the social security gold? how could provide insurance money and also can not consume too big cost them?
Summary of the invention
The purpose of this utility model is to disclose a kind of device based on recognition of face and vivo identification, to solve retired personnel's social security gold or endowment insurance money is provided problem, saves great amount of cost simultaneously.
According to the technical scheme that the utility model provides, described treating apparatus based on recognition of face and vivo identification comprises: the output terminal at the Network Interface Module of a video that is used for receiving automatic network or figure is connected a double-core chip SCO module; Connect a vivo identification module that is used for differentiating examined personnel's sign of life at the output terminal of double-core chip SCO module, connect the server of a canned data again at the output terminal of vivo identification module.SCO is a kind of UNIX network operating system.
It is a kind of based on the disposal system in face identification system and the vivo identification system that the utility model provides, it can carry out recognition of face and vivo identification to the video that comes from network, have sufficiently high discrimination and high recognition speed, with strong data-handling capacity, well compatible with existing monitor network energy, and working stability is easy to upgrading and safeguards that cost is low.
The running of the utility model total system realizes by following steps:
1, pass through people's face collecting device by the social security staff,, collect all retired personnels' of social security department facial image by Network Interface Module, and the information of carrying out registration, store in the server; , compare simultaneously with information networking above the second generation I.D.;
2, with the resulting information stores of step 1 to the social security department database, so that when carrying out authentications such as recognition of face and coupling operation, call;
3, download or buy the unified client software of stipulating by social security office;
4, Wai Di retired personnel installs software on computers, and log-on message is imported ID (identity number) card No. simultaneously;
5, the facial image by the camera collection client will be chosen automatically in video image that picture is the most clearly stored and compare with photo on the second generation I.D., determine it is same individual;
6, analyze contrast by people's face information of the personnel that step 1 and step 2 collected, confirm current personnel's sign of life by the facial muscle chattering frequency algorithm of independent research, carry out vivo identification by the vivo identification module.
Disposal system obvious technical effects of the present utility model, integrated recognition of face, vivo identification, network data base, the portrait combination, portrait detects sign of life automatically, multiple technologies such as video image acquisition and processing, obtain the long-distance video image information by network, compare fast, reach in time and determine by the purpose of assessor's true identity with the portrait in the database.Possess accurate computing method and strong data-handling capacity, thereby have sufficiently high discrimination and a high recognition speed, this system applies is in extensive range, be mainly used on the payroll management of social security department to retired personnel's retired endowment insurance money, save cost greatly, made things convenient for common people.
Description of drawings
Fig. 1 is a system chart of the present utility model.
Embodiment
As shown in the figure: the output terminal of the Network Interface Module of a video that is used for receiving automatic network or figure connect one be used for receiving the video image of automatic network and detect, the double-core chip SCO module of people's face information in the identification video image; Connect a vivo identification module that is used for differentiating examined personnel's sign of life at the output terminal of double-core chip SCO module, connect the server of a canned data again at the output terminal of vivo identification module.
The user passes to Network Interface Module to video image by the internet,
The video pictures information that the SCO module is received by network interface is carried out the recognition and verification of people's face, discerns people's face information of client simultaneously by the information in the canned data server.
In SCO module recognition and verification video image, these video images carry out vivo identification by the vivo identification module, confirm client's information simultaneously by the information in the canned data server.
The technical scheme of a kind of social security management system based on recognition of face and vivo identification of the present utility model is: it comprises the steps:
The A compressed video decoder: the stream medium data to Network Transmission carries out compressed video decoder, obtains digital image sequence;
The B pre-service: the original image to input comprises gray processing, the illumination compensation pre-service, and the quality of raising image obtains gray level image;
The C live body detects: adopt frame difference method and mixed Gaussian background modeling to define motion jointly the gray level image of input, or do not have the telemechanical generation; All then think have telemechanical to take place when the prospect connected region that two kinds of methods detect,, then carry out follow-up people's face and detect not have the motion generation, then do not carry out follow-up people's face and detect, and check if detect if detected the motion generation greater than threshold value;
D people's face location: front face is detected in real time, determine the position of people's face in image; Be included as feature calculation unit and taxon; Described for feature calculation unit is that gray level image to be detected is carried out convergent-divergent, exhaustive search candidate face window, the microstructure features of each window of calculated description, and it is passed to AdaBoost neural network classifier unit adjudicate;
E organ location; The organ location is to determine the position of people's face in image, comprises eyes, two eyebrows, nose, face, the location of lower jaw; Face shape facility and AdaBoost sorter according to people's face the zone of detected people's face window carry out eyes to C in the step, two eyebrows, and nose, face, the location of lower jaw for potential pseudo-organ, adopts the discrimination principle of maximum a posteriori probability to carry out filtering;
F normalization: according to the positional information of organ, try to achieve normalized gray level image, it is that image is comprised rotation, convergent-divergent, and shearing manipulation makes the eyes level, and the height of lower jaw is certain;
G feature extraction: from whole people's face, extract people's face and lose feature, comprise naked eyelid, eyebrow, eyes, nose, mouth face component: utilize principal component method to extract the feature of face component;
H people's face comparison: draw human face similarity degree: it is in known face database people's face to be identified to be adopted the calculating similarity and carries out multimodal overall recognition of face and local recognition of face by the method for sequencing of similarity.
The effect of a kind of social security management system based on recognition of face and vivo identification of the utility model is: reduce the long-range cost that retired personnel's identity is determined, to social security department to the retired endowment insurance money more speed the retired personnel in other places, high efficiency managing.
Advantage is: user's acceptance level height, because the retired personnel in the other places adopts old insurance money expense back and forth than higher, and trouble, so this utility model is put forth effort on vast user greatly.
Benefit to social security department is that native system can be realized long-range identification, than traditional recognition of face, fingerprint recognition is more preferably, native system has added the vivo identification system, and personnel are carried out the identification of sign of life, has overcome the situation of in the past falsely claiming as one's own social security gold or endowment insurance money.
The utility model also has than higher extendability, the correlation technique of the utility model research and development can also be applied to bank easily, security fields such as public security (as auxiliary monitoring and pursue and capture an escaped prisoner etc.), even can be applied in the break in traffic rules and regulations monitoring, extremely strong expandability therefore had.
AdaBoost is a kind of of Boosting algorithm, and its main thought is to distribute a weight to each training sample, shows that it is selected into the probability of training set by certain Weak Classifier, and weight is made as 1/m when initial, and m is a number of samples.Train on training set with a weak typing algorithm, the training back is adjusted sample weights, the sample weights of failure to train increases, and trains successful sample weights to reduce, and makes sorting algorithm concentrate strength on the sample of failure to train is learnt in the next round training.Then, continue training on the training set after weight is upgraded, constantly adjust sample weights, move in circles, thereby obtain a series of Weak Classifier.These Weak Classifiers just constitute assembled classifier, and the generation that assembled classifier finally predicts the outcome has been adopted the ballot of weight mode, and weight is exactly the accuracy rate of each Weak Classifier.This method does not require that single sorter has high discrimination, but has then had high discrimination through the assembled classifier of multiple Classifiers Combination.
Claims (1)
1, a kind for the treatment of apparatus based on recognition of face and vivo identification is characterized in that, described treating apparatus comprises: the output terminal at the Network Interface Module of a video that is used for receiving automatic network or figure connects double-core chip SCO module; Connect a vivo identification module that is used for differentiating examined personnel's sign of life at the output terminal of double-core chip SCO module, connect the server of a canned data again at the output terminal of vivo identification module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN200820214488U CN201374072Y (en) | 2008-12-19 | 2008-12-19 | Processor based on face recognition and living body recognition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN200820214488U CN201374072Y (en) | 2008-12-19 | 2008-12-19 | Processor based on face recognition and living body recognition |
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| Publication Number | Publication Date |
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| CN201374072Y true CN201374072Y (en) | 2009-12-30 |
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| Application Number | Title | Priority Date | Filing Date |
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| CN200820214488U Expired - Fee Related CN201374072Y (en) | 2008-12-19 | 2008-12-19 | Processor based on face recognition and living body recognition |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103310590A (en) * | 2012-03-06 | 2013-09-18 | 上海骏聿数码科技有限公司 | System and method for driver fatigue analysis and early-warning |
| CN106971140A (en) * | 2016-12-05 | 2017-07-21 | 天津灵隆科技有限公司 | A kind of face identification system and recognition methods |
-
2008
- 2008-12-19 CN CN200820214488U patent/CN201374072Y/en not_active Expired - Fee Related
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103310590A (en) * | 2012-03-06 | 2013-09-18 | 上海骏聿数码科技有限公司 | System and method for driver fatigue analysis and early-warning |
| CN106971140A (en) * | 2016-12-05 | 2017-07-21 | 天津灵隆科技有限公司 | A kind of face identification system and recognition methods |
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Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20091230 Termination date: 20151219 |
|
| EXPY | Termination of patent right or utility model |