CN111986794B - Anti-fake registering method and device based on face recognition, computer equipment and medium - Google Patents
Anti-fake registering method and device based on face recognition, computer equipment and medium Download PDFInfo
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Abstract
The invention relates to the technical field of digital medical treatment, and provides an anti-fake registering method, device, computer equipment and medium based on face recognition, which comprises the following steps: establishing a multi-user social relationship graph; acquiring a front face image of a registered person, and identifying registered person identity information of the registered person according to the front face image; judging whether the registered person and the doctor are the same person or not according to the registered person identification information and the second identity information of the doctor; when the registered person and the doctor person are not the same person, inquiring the multi-user social relationship graph according to the identity information of the registered person, and determining whether the registered person and the doctor person are related persons according to an inquiry result; and when the registered person and the doctor person are determined to be related according to the query result, generating a registered list for the doctor person. The invention can solve the problem that the medical insurance card is stolen and improve registration efficiency.
Description
Technical Field
The invention relates to the technical field of digital medical treatment, in particular to an anti-fake registering method, device, computer equipment and medium based on face recognition.
Background
The existing face recognition registering system can recognize whether the person is the person or not by comparing acquired portrait with identity document information, and prevents the patient information from being falsely used. However, in real life, the situation that the person cannot register on site often exists, so that the existing face recognition registering system cannot meet the problem that the person registers in emergency, and therefore the person cannot register for the patient in time, and registering efficiency is low.
Disclosure of Invention
In view of the above, it is necessary to provide an anti-counterfeit registration method, device, computer equipment and medium based on face recognition, which can solve the problem that medical insurance cards are stolen and improve registration efficiency.
The first aspect of the invention provides an anti-counterfeiting registering method based on face recognition, which comprises the following steps:
establishing a multi-user social relationship graph;
acquiring a front face image of a registered person, and identifying registered person identity information of the registered person according to the front face image;
Judging whether the registered person and the doctor are the same person or not according to the registered person identification information and the second identity information of the doctor;
When the registered person and the doctor person are not the same person, inquiring the multi-user social relationship graph according to the identity information of the registered person, and determining whether the registered person and the doctor person are related persons according to an inquiry result;
And when the registered person and the doctor person are determined to be related according to the query result, generating a registered list for the doctor person.
According to an optional embodiment of the invention, the establishing a multi-user social relationship graph comprises:
Configuring a data acquisition interface;
Acquiring user information of a plurality of users through the data acquisition interface, wherein the user information comprises: personnel relationship, residence and call data;
Establishing a first social relation graph according to the call data, wherein the first social relation graph takes a user as a vertex, and an edge is established between two vertices with the call data;
Updating the first social relation graph according to the personnel relation to obtain a second social relation graph;
and deleting the non-resident matched edges in the second social relationship graph to obtain a multi-user social relationship graph.
According to an optional embodiment of the present invention, the identifying the registration person identifier information of the registration person according to the front face image includes:
Inputting the front face image into a pre-trained face identity recognition model for recognition to obtain the name and the ID card number of the registered person;
Determining a resident place of the registered person matched with the name and the ID card number of the registered person according to a household registration information database;
and determining the name of the registered person, the ID card number of the registered person and the resident location of the registered person as the registered person identity information of the registered person.
According to an optional embodiment of the present invention, the querying the multi-user social relationship graph according to the identity information of the registered person, and determining whether the registered person and the doctor person are related persons according to the query result includes:
Inquiring the multi-user social relationship graph by taking the names of the consultants as inquiry keywords to obtain a plurality of associated names;
Identifying whether a target name identical to the registered person name exists in the plurality of associated names;
When the target names which are the same as the names of the registered persons exist in the plurality of associated names, judging whether the resident address of the doctor is the doctor address of the doctor or not;
and when the resident address of the doctor is the doctor address of the doctor, determining that the registered person and the doctor are related.
According to an alternative embodiment of the invention, the method further comprises:
when the registered person and the doctor person are related according to the query result, receiving a voice response of the registered person aiming at a preset problem;
Recognizing the voice answer to obtain a voice text;
extracting key fields in the voice text;
Checking the key field;
and generating a registration order for the consultant and updating the multi-user social relationship graph in response to successful verification of the key field.
According to an alternative embodiment of the invention, the method further comprises:
when the registered person and the patient are the same person, identifying the pain level of the registered person according to the front face image;
generating a registration order for the registered person according to the pain level.
According to an alternative embodiment of the present invention, the identifying the pain level of the registered person from the frontal face image comprises:
Preprocessing the front face image;
Converting the preprocessed front face image into a gray image;
Inputting the gray level image into a pre-trained pain level identification model for identification to obtain a pain level;
Wherein the training process of the pain class identification model comprises the following steps: setting a plurality of pain grades from low to high according to a pain grade scale, and respectively acquiring corresponding pain expression images according to different pain grades; preprocessing all pain expression images by using dlib tools to obtain RGB images; converting the RGB image into a gray level image to obtain a pain expression training data set; and training a pain class recognition model based on the pain expression training data set.
A second aspect of the present invention provides an anti-counterfeit registration device based on face recognition, the device comprising:
the map establishing module is used for establishing a multi-user social relationship map;
the image recognition module is used for acquiring a front face image of the registered person and recognizing registered person identity information of the registered person according to the front face image;
The identity judging module is used for judging whether the registered person and the doctor are the same person or not according to the registered person identity information and the second identity information of the doctor;
The map query module is used for querying the multi-user social relationship map according to the identity information of the registered person when the registered person and the doctor are determined not to be the same person, and determining whether the registered person and the doctor are related persons according to a query result;
And the registration list generation module is used for generating a registration list for the doctor when the registration person and the doctor person are determined to be related according to the query result.
A third aspect of the present invention provides a computer apparatus comprising:
A memory for storing a computer program;
And the processor is used for realizing the anti-fake registering method based on the face recognition when executing the computer program.
A fourth aspect of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the face recognition based anti-impersonation registration method.
In summary, according to the anti-counterfeit registration method, the anti-counterfeit registration device, the computer equipment and the medium based on the face recognition, the registration person identity information of the registration person is obtained by recognizing the front face image of the registration person, and when the registration person is determined not to be the same person as the patient according to the second identity information of the patient, the established multi-user social relationship graph is inquired according to the identity information of the registration person, so that whether the registration person and the patient are related persons or not is determined according to the inquiry result; and generating a registration list for the person to be treated when the registration person and the person to be treated are determined to be related according to the query result. The face recognition technology is applied to the medical field, so that the problem that the medical insurance card is stolen can be solved, a registration list can be quickly generated for a doctor, and the registration efficiency is improved.
Drawings
Fig. 1 is a flowchart of an anti-counterfeit registration method based on face recognition according to an embodiment of the present invention.
Fig. 2 is a block diagram of an anti-counterfeit registration device based on face recognition according to a second embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It should be noted that, without conflict, the embodiments of the present invention and features in the embodiments may be combined with each other.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Fig. 1 is a flowchart of an anti-counterfeit registration method based on face recognition according to an embodiment of the present invention. The anti-counterfeiting registering method based on face recognition specifically comprises the following steps, the sequence of the steps in the flow chart can be changed according to different requirements, and some steps can be omitted.
S11, establishing a multi-user social relationship graph.
The computer device may divide the regions in advance, and establish a multi-user social relationship graph of each center point with each divided region as a center point. The regions are divided according to provinces, and a multi-user social relationship graph of each province is established, or the regions are divided according to cities, and a multi-user social relationship graph of each city is established. The multi-user social relationship graph is used for determining the association relationship between users, so as to determine whether the users are related people or not.
In an alternative embodiment, the establishing the multi-user social relationship graph includes:
Configuring a data acquisition interface;
Acquiring user information of a plurality of users through the data acquisition interface, wherein the user information comprises: personnel relationship, residence and call data;
Establishing a first social relation graph according to the call data, wherein the first social relation graph takes a user as a vertex, and an edge is established between two vertices with the call data;
Updating the first social relation graph according to the personnel relation to obtain a second social relation graph;
and deleting the non-resident matched edges in the second social relationship graph to obtain a multi-user social relationship graph.
In this alternative embodiment, the computer device may obtain the user information of the plurality of users from a public security database, a communications device operator database, or the like. The user information includes: personnel relationship, residence, and call data. Wherein, the personnel relationship may include a relative relationship, a colleague relationship, and a colleague relationship. The call data may include an international mobile subscriber identity (International Mobile Subscriber Identity, IMSI) of the calling party, and an international mobile subscriber identity (International Mobile Subscriber Identity, IMSI) of the called party.
The computer equipment establishes an edge between any two users with call data, so that a first social relation graph can be initially established. And then, aiming at two users on any side, if the two users have personnel relations, reserving the side, and if the two users do not have personnel relations, deleting the side, so that the first social relation graph is updated to a second social relation graph. And finally, aiming at two users on any side in the second social relation graph, if the residences of the two users are the same, reserving the side, and if the residences of the two users are different, deleting the side, so that the second social relation graph is updated to be a multi-user social relation graph.
S12, acquiring a front face image of the registered person, and identifying registered person identity information of the registered person according to the front face image.
The user registering at the registering wide port is called a registering person, an image acquisition device, for example, a high-definition digital image acquisition device is installed in the registering wide port, and a front face image of the registering person is acquired through the image acquisition device and transmitted to the computer device.
The computer equipment identifies the registration person identity information of the registration person according to the front face image of the registration person.
In an optional embodiment, the identifying the registration person identifier information of the registration person according to the front face image includes:
Inputting the front face image into a pre-trained face identity recognition model for recognition to obtain the name and the ID card number of the registered person;
Determining a resident place of the registered person matched with the name and the ID card number of the registered person according to a household registration information database;
and determining the name of the registered person, the ID card number of the registered person and the resident location of the registered person as the registered person identity information of the registered person.
In this alternative embodiment, the computer device may obtain, in advance, identification card images of a plurality of users from the public security database, cut face images from the identification card images, and train the face identification model based on the cut face images and the corresponding names and identification card numbers as the data set. The training process of the face identification model is the prior art, and the invention is not described in detail.
The name, the ID card number and the residence of the user are recorded in the household registration information database.
The computer equipment acquires the registration person identity information of the registration person, so that whether the registration person is stolen or faked to use the medical insurance card can be conveniently determined according to the registration person identity information.
S13, judging whether the registered person and the doctor are the same person or not according to the registered person identification information and the second identity information of the doctor.
The computer device can read information in the medical insurance card submitted by the registration person in the registration window to determine second identity information of the doctor. The second identity information comprises the name and the number of the consultant.
The computer equipment compares whether the names of the registered persons and the names of the attendees are the same, and simultaneously compares whether the identification card numbers of the registered persons and the identification card numbers of the attendees are the same. When the names of the registered persons and the names of the visiting persons are the same, and the identification card numbers of the registered persons and the identification card numbers of the visiting persons are the same, the registered persons and the visiting persons are determined to be the same person. When the names of the registered persons and the names of the attendees are different, or the identification card numbers of the registered persons and the identification card numbers of the attendees are different, the registered persons and the attendees are determined to be different.
S14, when the registered person and the doctor person are not the same person, inquiring the multi-user social relationship graph according to the identity information of the registered person, and determining whether the registered person and the doctor person are related persons according to an inquiring result.
The computer equipment determines whether the registered person and the doctor person are related persons or not by inquiring the multi-user social relationship graph, so that whether the registered person is a medical insurance card of a pirate or imposter doctor person or not is further determined, and the use safety of the medical insurance card is ensured.
The query result is that the query is empty, or a plurality of associated names are queried.
In an optional embodiment, the querying the multi-user social relationship graph according to the identity information of the registered person, and determining whether the registered person and the doctor person are related according to the query result includes:
Inquiring the multi-user social relationship graph by taking the names of the consultants as inquiry keywords to obtain a plurality of associated names;
Identifying whether a target name identical to the registered person name exists in the plurality of associated names;
When the target names which are the same as the names of the registered persons exist in the plurality of associated names, judging whether the resident address of the doctor is the doctor address of the doctor or not;
and when the resident address of the doctor is the doctor address of the doctor, determining that the registered person and the doctor are related.
In this alternative embodiment, the registered person and the attendant are determined not to be related persons when the computer device recognizes that the same target name as the registered person name does not exist in the plurality of associated names, or when it is determined that the resident address of the attendant is not the attendant address of the attendant.
S15, when the registered person and the person related to the doctor are determined according to the query result, a registered list is generated for the doctor.
And when the computer equipment determines that the registered person is related to the doctor according to the query result, the registered person can be determined to have no fraudulent use or fraudulent use of the medical insurance card, and a registration list is generated for the doctor. When the computer equipment determines that the registered person and the doctor person are irrelevant according to the query result, the registered person can be determined to have the actions of embezzling or impersonating the medical insurance card, and a registered list is not generated for the doctor person.
In an alternative embodiment, when the registered person is determined to be related to the doctor person according to the query result, the method further includes:
Receiving a voice response of a registration person aiming at a preset problem;
Recognizing the voice answer to obtain a voice text;
extracting key fields in the voice text;
Checking the key field;
and generating a registration order for the consultant and updating the multi-user social relationship graph in response to successful verification of the key field.
The preset questions can be what the medical insurance payment unit is, how long the medical insurance payment service period is, what the balance is in the medical insurance card, and the like.
The registration window can be further provided with a voice acquisition device, the voice acquisition device outputs the preset questions, and the voice answers of the registered person to the preset questions are received. The voice collection equipment transmits the voice answer to the computer equipment, and the computer equipment adopts a voice recognition algorithm to recognize the voice answer to obtain a voice text; or the voice collecting equipment adopts a voice recognition algorithm to recognize the voice answer to obtain a voice text, and the voice text is transmitted to the computer equipment.
And the computer equipment extracts the key field and then matches the key field with the medical insurance card information, when the key field is matched with the medical insurance card information, the key field is determined to be successfully checked, and when the key field is not matched with the medical insurance card information, the key field is determined to be failed to be checked.
After the key field verification is successful, the computer equipment can update the multi-user social relation graph according to the name of the registered person, the identification card number of the registered person and the residence of the registered person, so that when the subsequent registered person registers for the visiting person again, the registered person and the visiting person related person can be quickly determined according to the updated multi-user social relation graph, a registration list can be quickly generated for the visiting person, and the registration efficiency is improved.
After the computer equipment determines that the verification of the key field fails, an alarm instruction can be triggered, and the face image of the registered person is sent to the credit investigation computer equipment.
In an alternative embodiment, the method further comprises:
when the registered person and the patient are the same person, identifying the pain level of the registered person according to the front face image;
generating a registration order for the registered person according to the pain level.
In this alternative embodiment, the computer device further identifies the pain level based on the front face image of the registered person when the registered person determines that the registered person is present. When the identified pain level is higher, indicating that the disease condition of the registered person is serious, and immediately visiting the doctor, generating a first-level registration list for the doctor; when the identified pain level is lower, the condition of the registered person is slight, and the registered person does not need to visit immediately, and a lowest-level registered list is generated for the registered person.
Different grades of registration orders are generated by identifying pain grades, so that medical resources can be optimized, and the diagnosis experience of a doctor is improved.
In an alternative embodiment, said identifying the pain level of the registered person from the frontal face image comprises:
Preprocessing the front face image;
Converting the preprocessed front face image into a gray image;
and inputting the gray level image into a pre-trained pain level identification model for identification, and obtaining the pain level.
In this alternative embodiment, the computer device performs pain level recognition on the gray level image by using the pain level recognition model, obtains probabilities corresponding to a plurality of pain levels, and selects a pain level corresponding to a maximum probability as the pain level of the registered person.
In an alternative embodiment, the training process of the pain class identification model comprises:
setting a plurality of pain grades from low to high according to a pain grade scale, and respectively acquiring corresponding pain expression images according to different pain grades;
preprocessing all pain expression images by using dlib tools to obtain RGB images;
converting the RGB image into a gray level image to obtain a pain expression training data set;
And training a pain class recognition model based on the pain expression training data set.
The pain expression image set is built by dividing the table according to the pain degree of the world health organization, taking 5 pain grades, dividing the pain grades into five grades from 0 to IV in sequence, respectively corresponding to painless, mild pain, moderate pain, severe pain and intolerable five pain grades. According to 6:2:2 are sequentially divided into a training set, a test set and a verification set according to the proportion, a pain data set is formed and is marked as V= { V1, V2, V3, … …, vN, … … and vN }, wherein vN represents the information of the nth pain image, and N represents the total number of images in the data set V of the pain expression image. vn contains pain level labels (labeled pain level 0-IV), two-dimensional pixel arrays (64 x 64 pixels), and image usage divisions (training, testing, validation represented by 0,1, 2, respectively). And training a neural network model for analyzing the pain level based on the pain expression image set to obtain a pain level identification model.
In an alternative embodiment, after said deriving the pain level, the method further comprises:
obtaining a self-pain rating of the registered person;
Calculating a difference between the self-pain level and the pain level;
Judging whether the difference value is smaller than a preset difference value threshold value or not;
And when the difference value is determined to be greater than or equal to the preset difference value threshold value, adding the front face image and the pain level into the pain expression training data set, and retraining a pain level identification model until the difference value between the pain level identified by the pain level identification model and the self-pain level is smaller than the preset difference value threshold value.
In this alternative embodiment, after each recognition of the pain level, the computer device automatically adds the pain level and the corresponding gray level image to the pain expression training data set, and updates the pain level recognition model, so as to improve the recognition accuracy of the pain level recognition model. The difference between the self-described pain level and the identified pain level is used as a training target of the pain level identification model, so that the consistency of the business target and the model target is realized, the accuracy of pain level identification when the pain level identification model is applied to the actual application scene of pain level identification of a registered person can be further improved, and the pain level identification method has higher application value.
Acquiring registration person identity information of the registration person by identifying a front face image of the registration person, and inquiring an established multi-user social relationship graph according to the identity information of the registration person when the registration person is determined to be different from the doctor according to the second identity information of the registration person and the doctor, so as to determine whether the registration person and the doctor are related people according to an inquiry result; and generating a registration list for the person to be treated when the registration person and the person to be treated are determined to be related according to the query result. The face recognition technology is applied to the medical field, so that the problem that the medical insurance card is stolen can be solved, a registration list can be quickly generated for a doctor, and the registration efficiency is improved.
It is emphasized that, to further ensure the privacy and security of the multi-user social relationship graph, the multi-user social relationship graph may be stored in a node of the blockchain.
The anti-counterfeit registration method based on face recognition can be applied to intelligent medical treatment and can improve the construction of intelligent cities.
Fig. 2 is a block diagram of an anti-counterfeit registration device based on face recognition according to a second embodiment of the present invention.
In some embodiments, the face recognition-based anti-counterfeiting registering apparatus 20 may comprise a plurality of functional modules composed of computer program segments. The computer program of the individual program segments in the face recognition based anti-counterfeiting registration apparatus 20 may be stored in a memory of a computer device and executed by at least one processor to perform (see fig. 1 for details) the face recognition based anti-counterfeiting registration functions.
In this embodiment, the anti-counterfeiting registering apparatus 20 based on face recognition may be divided into a plurality of functional modules according to the functions performed by the apparatus. The functional module may include: the system comprises a map establishing module 201, an image identifying module 202, an identity judging module 203, a map inquiring module 204, a registration list generating module 205, a question and answer checking module 206, a pain identifying module 207 and a model training module 208. The module referred to in the present invention refers to a series of computer program segments capable of being executed by at least one processor and of performing a fixed function, stored in a memory. In the present embodiment, the functions of the respective modules will be described in detail in the following embodiments.
The graph establishment module 201 is configured to establish a multi-user social relationship graph.
The computer device may divide the regions in advance, and establish a multi-user social relationship graph of each center point with each divided region as a center point. The regions are divided according to provinces, and a multi-user social relationship graph of each province is established, or the regions are divided according to cities, and a multi-user social relationship graph of each city is established. The multi-user social relationship graph is used for determining the association relationship between users, so as to determine whether the users are related people or not.
In an alternative embodiment, the graph creation module 201 creates the multi-user social relationship graph includes:
Configuring a data acquisition interface;
Acquiring user information of a plurality of users through the data acquisition interface, wherein the user information comprises: personnel relationship, residence and call data;
Establishing a first social relation graph according to the call data, wherein the first social relation graph takes a user as a vertex, and an edge is established between two vertices with the call data;
Updating the first social relation graph according to the personnel relation to obtain a second social relation graph;
and deleting the non-resident matched edges in the second social relationship graph to obtain a multi-user social relationship graph.
In this alternative embodiment, the computer device may obtain user information for a plurality of users from a public security database, a communications device operator database. The user information includes: personnel relationship, residence, and call data. Wherein, the personnel relationship may include a relative relationship, a colleague relationship, and a colleague relationship. The call data may include an international mobile subscriber identity (International Mobile Subscriber Identity, IMSI) of the calling party, and an international mobile subscriber identity (International Mobile Subscriber Identity, IMSI) of the called party.
The computer equipment establishes an edge between any two users with call data, so that a first social relation graph can be initially established. And then, aiming at two users on any side, if the two users have personnel relations, reserving the side, and if the two users do not have personnel relations, deleting the side, so that the first social relation graph is updated to a second social relation graph. And finally, aiming at two users on any side in the second social relation graph, if the residences of the two users are the same, reserving the side, and if the residences of the two users are different, deleting the side, so that the second social relation graph is updated to be a multi-user social relation graph.
The image recognition module 202 is configured to obtain a front face image of a registered person, and recognize registration person identity information of the registered person according to the front face image.
The user registering at the registering wide port is called a registering person, an image acquisition device, for example, a high-definition digital image acquisition device is installed in the registering wide port, and a front face image of the registering person is acquired through the image acquisition device and transmitted to the computer device.
The computer equipment identifies the registration person identity information of the registration person according to the front face image of the registration person.
In an alternative embodiment, the image recognition module 202 recognizes the registered person identifier information of the registered person according to the front face image includes:
Inputting the front face image into a pre-trained face identity recognition model for recognition to obtain the name and the ID card number of the registered person;
Determining a resident place of the registered person matched with the name and the ID card number of the registered person according to a household registration information database;
and determining the name of the registered person, the ID card number of the registered person and the resident location of the registered person as the registered person identity information of the registered person.
In this alternative embodiment, the computer device may obtain, in advance, identification card images of a plurality of users from the public security database, cut face images from the identification card images, and train the face identification model based on the cut face images and the corresponding names and identification card numbers as the data set. The training process of the face identification model is the prior art, and the invention is not described in detail.
The name, the ID card number and the residence of the user are recorded in the household registration information database.
The computer equipment acquires the registration person identity information of the registration person, so that whether the registration person is stolen or faked to use the medical insurance card can be conveniently determined according to the registration person identity information.
The identity determining module 203 is configured to determine whether the registered person and the person to be treated are the same person according to the registered person identity information and the second identity information of the person to be treated.
The computer device can read information in the medical insurance card submitted by the registration person in the registration window to determine second identity information of the doctor. The second identity information comprises the name and the number of the consultant.
The computer equipment compares whether the names of the registered persons and the names of the attendees are the same, and simultaneously compares whether the identification card numbers of the registered persons and the identification card numbers of the attendees are the same. When the names of the registered persons and the names of the visiting persons are the same, and the identification card numbers of the registered persons and the identification card numbers of the visiting persons are the same, the registered persons and the visiting persons are determined to be the same person. When the names of the registered persons and the names of the attendees are different, or the identification card numbers of the registered persons and the identification card numbers of the attendees are different, the registered persons and the attendees are determined to be different.
The map query module 204 is configured to query the multi-user social relationship map according to the identity information of the registered person when it is determined that the registered person and the person to be treated are not the same person, and determine whether the registered person and the person to be treated are related persons according to a query result.
The computer equipment determines whether the registered person and the doctor person are related persons or not by inquiring the multi-user social relationship graph, so that whether the registered person is a medical insurance card of a pirate or imposter doctor person or not is further determined, and the use safety of the medical insurance card is ensured.
The query result is that the query is empty, or a plurality of associated names are queried.
In an alternative embodiment, the map query module 204 queries the multi-user social relationship map according to the identity information of the registered person, and determines whether the registered person and the doctor person are related persons according to the query result includes:
Inquiring the multi-user social relationship graph by taking the names of the consultants as inquiry keywords to obtain a plurality of associated names;
Identifying whether a target name identical to the registered person name exists in the plurality of associated names;
When the target names which are the same as the names of the registered persons exist in the plurality of associated names, judging whether the resident address of the doctor is the doctor address of the doctor or not;
and when the resident address of the doctor is the doctor address of the doctor, determining that the registered person and the doctor are related.
In this alternative embodiment, the registered person and the attendant are determined not to be related persons when the computer device recognizes that the same target name as the registered person name does not exist in the plurality of associated names, or when it is determined that the resident address of the attendant is not the attendant address of the attendant.
The registration list generation module 205 is configured to generate a registration list for the doctor person when the registration person and the doctor person are determined to be related according to the query result.
And when the computer equipment determines that the registered person is related to the doctor according to the query result, the registered person can be determined to have no fraudulent use or fraudulent use of the medical insurance card, and a registration list is generated for the doctor. When the computer equipment determines that the registered person and the doctor person are irrelevant according to the query result, the registered person can be determined to have the actions of embezzling or impersonating the medical insurance card, and a registered list is not generated for the doctor person.
The question and answer verification module 206 is configured to receive a voice answer of the registered person for a preset question when the registered person and the doctor person are determined to be related according to a query result; recognizing the voice answer to obtain a voice text; extracting key fields in the voice text; and checking the key field.
The registration form generation module 205 is further configured to generate a registration form for the interviewee and update the multi-user social relationship graph in response to a successful verification of the key field.
The preset questions can be what the medical insurance payment unit is, how long the medical insurance payment service period is, what the balance is in the medical insurance card, and the like.
The registration window can be further provided with a voice acquisition device, the voice acquisition device outputs the preset questions, and the voice answers of the registered person to the preset questions are received. The voice collection equipment transmits the voice answer to the computer equipment, and the computer equipment adopts a voice recognition algorithm to recognize the voice answer to obtain a voice text; or the voice collecting equipment adopts a voice recognition algorithm to recognize the voice answer to obtain a voice text, and the voice text is transmitted to the computer equipment.
And the computer equipment extracts the key field and then matches the key field with the medical insurance card information, when the key field is matched with the medical insurance card information, the key field is determined to be successfully checked, and when the key field is not matched with the medical insurance card information, the key field is determined to be failed to be checked.
After the key field verification is successful, the computer equipment can update the multi-user social relation graph according to the name of the registered person, the identification card number of the registered person and the residence of the registered person, so that when the subsequent registered person registers for the visiting person again, the registered person and the visiting person related person can be quickly determined according to the updated multi-user social relation graph, a registration list can be quickly generated for the visiting person, and the registration efficiency is improved.
After the computer equipment determines that the verification of the key field fails, an alarm instruction can be triggered, and the face image of the registered person is sent to the credit investigation computer equipment.
The pain identification module 207 is configured to identify a pain level of the registered person according to the frontal face image when it is determined that the registered person is the same person as the doctor;
the registration form generation module 205 is further configured to generate a registration form for the registrant according to the pain level.
In this alternative embodiment, the computer device further identifies the pain level based on the front face image of the registered person when the registered person determines that the registered person is present. When the identified pain level is higher, indicating that the disease condition of the registered person is serious, and immediately visiting the doctor, generating a first-level registration list for the doctor; when the identified pain level is lower, the condition of the registered person is slight, and the registered person does not need to visit immediately, and a lowest-level registered list is generated for the registered person.
Different grades of registration orders are generated by identifying pain grades, so that medical resources can be optimized, and the diagnosis experience of a doctor is improved.
In an alternative embodiment, the pain identification module 207 identifies the pain class of the registered person from the frontal face image comprises:
Preprocessing the front face image;
Converting the preprocessed front face image into a gray image;
and inputting the gray level image into a pre-trained pain level identification model for identification, and obtaining the pain level.
In this alternative embodiment, the computer device performs pain level recognition on the gray level image by using the pain level recognition model, obtains probabilities corresponding to a plurality of pain levels, and selects a pain level corresponding to a maximum probability as the pain level of the registered person.
The model training module 208 is configured to train a pain class identification model. In an alternative embodiment, the training process of the pain class identification model comprises:
setting a plurality of pain grades from low to high according to a pain grade scale, and respectively acquiring corresponding pain expression images according to different pain grades;
preprocessing all pain expression images by using dlib tools to obtain RGB images;
converting the RGB image into a gray level image to obtain a pain expression training data set;
And training a pain class recognition model based on the pain expression training data set.
The pain expression image set is built by dividing the table according to the pain degree of the world health organization, taking 5 pain grades, dividing the pain grades into five grades from 0 to IV in sequence, respectively corresponding to painless, mild pain, moderate pain, severe pain and intolerable five pain grades. According to 6:2:2 are sequentially divided into a training set, a test set and a verification set according to the proportion, a pain data set is formed and is marked as V= { V1, V2, V3, … …, vN, … … and vN }, wherein vN represents the information of the nth pain image, and N represents the total number of images in the data set V of the pain expression image. vn contains pain level labels (labeled pain level 0-IV), two-dimensional pixel arrays (64 x 64 pixels), and image usage divisions (training, testing, validation represented by 0,1, 2, respectively). And training a neural network model for analyzing the pain level based on the pain expression image set to obtain a pain level identification model.
The model training module 208 is further configured to obtain a self-pain level of the registered person; calculating a difference between the self-pain level and the pain level; judging whether the difference value is smaller than a preset difference value threshold value or not; and when the difference value is determined to be greater than or equal to the preset difference value threshold value, adding the front face image and the pain level into the pain expression training data set, and retraining a pain level identification model until the difference value between the pain level identified by the pain level identification model and the self-pain level is smaller than the preset difference value threshold value.
In this alternative embodiment, after each recognition of the pain level, the computer device automatically adds the pain level and the corresponding gray level image to the pain expression training data set, and updates the pain level recognition model, so as to improve the recognition accuracy of the pain level recognition model. The difference between the self-described pain level and the identified pain level is used as a training target of the pain level identification model, so that the consistency of the business target and the model target is realized, the accuracy of pain level identification when the pain level identification model is applied to the actual application scene of pain level identification of a registered person can be further improved, and the pain level identification method has higher application value.
Acquiring registration person identity information of the registration person by identifying a front face image of the registration person, and inquiring an established multi-user social relationship graph according to the identity information of the registration person when the registration person is determined to be different from the doctor according to the second identity information of the registration person and the doctor, so as to determine whether the registration person and the doctor are related people according to an inquiry result; and generating a registration list for the person to be treated when the registration person and the person to be treated are determined to be related according to the query result. The face recognition technology is applied to the medical field, so that the problem that the medical insurance card is stolen can be solved, a registration list can be quickly generated for a doctor, and the registration efficiency is improved.
It is emphasized that, to further ensure the privacy and security of the multi-user social relationship graph, the multi-user social relationship graph may be stored in a node of the blockchain.
Fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present invention. In the preferred embodiment of the present invention, the computer device 3 includes a memory 31, at least one processor 32, at least one communication bus 33, and a transceiver 34. The computer device 3 of the invention can be applied to intelligent medical treatment to improve the construction of intelligent cities.
It will be appreciated by those skilled in the art that the configuration of the computer device shown in fig. 3 is not limiting of the embodiments of the present invention, and that either a bus-type configuration or a star-type configuration is possible, and that the computer device 3 may include more or less other hardware or software than that shown, or a different arrangement of components.
In some embodiments, the computer device 3 is a computer device capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The computer device 3 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client by way of a keyboard, mouse, remote control, touch pad, or voice control device, such as a personal computer, tablet, smart phone, digital camera, etc.
It should be noted that the computer device 3 is only used as an example, and other electronic products that may be present in the present invention or may be present in the future are also included in the scope of the present invention by way of reference.
In some embodiments, the memory 31 has stored therein a computer program which, when executed by the at least one processor 32, implements all or part of the steps in the face recognition based anti-counterfeiting registration method as described. The Memory 31 includes Read-Only Memory (ROM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable rewritable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic tape Memory, or any other medium that can be used for carrying or storing data.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
In some embodiments, the at least one processor 32 is a Control Unit (Control Unit) of the computer device 3, connects the various components of the entire computer device 3 using various interfaces and lines, and performs various functions and processes of the computer device 3 by running or executing programs or modules stored in the memory 31, and invoking data stored in the memory 31. For example, the at least one processor 32, when executing the computer program stored in the memory, implements all or part of the steps of the face recognition-based anti-counterfeiting registration method described in embodiments of the present invention; or realize all or part of functions of the anti-counterfeiting registering device based on face recognition. The at least one processor 32 may be comprised of integrated circuits, such as a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functionality, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like.
In some embodiments, the at least one communication bus 33 is arranged to enable connected communication between the memory 31 and the at least one processor 32 or the like.
Although not shown, the computer device 3 may further comprise a power source (such as a battery) for powering the various components, preferably the power source is logically connected to the at least one processor 32 via a power management means, whereby the functions of managing charging, discharging, and power consumption are performed by the power management means. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The computer device 3 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described in detail herein.
The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional modules described above are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or processor (processor) to perform portions of the methods described in the various embodiments of the invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it will be obvious that the term "comprising" does not exclude other elements or that the singular does not exclude a plurality. A plurality of units or means recited in the apparatus claims can also be implemented by means of one unit or means in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (9)
1. An anti-counterfeiting registering method based on face recognition, which is characterized by comprising the following steps:
Establishing the multi-user social relationship graph comprises the following steps: configuring a data acquisition interface; acquiring user information of a plurality of users through the data acquisition interface, wherein the user information comprises: personnel relationship, residence and call data; establishing a first social relation graph according to the call data, wherein the first social relation graph takes a user as a vertex, and an edge is established between two vertices with the call data; updating the first social relation graph according to the personnel relation to obtain a second social relation graph; deleting the non-resident matched edges in the second social relationship graph to obtain a multi-user social relationship graph; the multi-user social relationship graph comprises multi-user social relationship graphs of all center points taking all divided areas as center points in advance;
acquiring a front face image of a registered person, and identifying registered person identity information of the registered person according to the front face image;
Judging whether the registered person and the doctor are the same person or not according to the registered person identification information and the second identity information of the doctor;
When the registered person and the doctor person are not the same person, inquiring the multi-user social relationship graph according to the identity information of the registered person, and determining whether the registered person and the doctor person are related persons according to an inquiry result;
when the registered person and the doctor person are determined to be related according to the query result, generating a registered list for the doctor person;
When the registered person and the patient are the same person, identifying the pain level of the registered person according to the front face image; generating a registration order for the registered person according to the pain level; the identifying the pain level of the registered person according to the frontal face image comprises: preprocessing the front face image; converting the preprocessed front face image into a gray image; inputting the gray level image into a pre-trained pain level identification model for identification to obtain a pain level; obtaining the registered person from the pain class; calculating a difference between the self-pain level and the pain level; and when the difference value is greater than or equal to a preset difference value threshold value, retraining the pain level identification model according to the front face image and the pain level.
2. The face recognition-based anti-counterfeiting registering method according to claim 1, wherein the identifying registering person identification information of the registering person according to the front face image comprises:
Inputting the front face image into a pre-trained face identity recognition model for recognition to obtain the name and the ID card number of the registered person;
Determining a resident place of the registered person matched with the name and the ID card number of the registered person according to a household registration information database;
and determining the name of the registered person, the ID card number of the registered person and the resident location of the registered person as the registered person identity information of the registered person.
3. The anti-counterfeiting registering method based on face recognition according to claim 2, wherein the querying the multi-user social relationship graph according to the identity information of the registered person, and determining whether the registered person and the doctor person are related persons according to a query result comprises:
Inquiring the multi-user social relationship graph by taking the names of the consultants as inquiry keywords to obtain a plurality of associated names;
Identifying whether a target name identical to the registered person name exists in the plurality of associated names;
When the target names which are the same as the names of the registered persons exist in the plurality of associated names, judging whether the resident address of the doctor is the doctor address of the doctor or not;
and when the resident address of the doctor is the doctor address of the doctor, determining that the registered person and the doctor are related.
4. The face recognition-based anti-counterfeiting registering method according to claim 3, wherein the method further comprises:
when the registered person and the doctor person are related according to the query result, receiving a voice response of the registered person aiming at a preset problem;
Recognizing the voice answer to obtain a voice text;
extracting key fields in the voice text;
Checking the key field;
and generating a registration order for the consultant and updating the multi-user social relationship graph in response to successful verification of the key field.
5. A face recognition-based anti-counterfeiting registration method according to any one of claims 1 to 4, wherein the method further comprises:
when the registered person and the patient are the same person, identifying the pain level of the registered person according to the front face image;
generating a registration order for the registered person according to the pain level.
6. The face recognition-based anti-counterfeiting registration method according to claim 1, wherein the training process of the pain class recognition model comprises: setting a plurality of pain grades from low to high according to a pain grade scale, and respectively acquiring corresponding pain expression images according to different pain grades; preprocessing all pain expression images by using dlib tools to obtain RGB images; converting the RGB image into a gray level image to obtain a pain expression training data set; and training a pain class recognition model based on the pain expression training data set.
7. An anti-counterfeiting registering device based on face recognition, which is characterized by comprising:
The map establishing module is used for establishing a multi-user social relationship map and comprises the following steps: configuring a data acquisition interface; acquiring user information of a plurality of users through the data acquisition interface, wherein the user information comprises: personnel relationship, residence and call data; establishing a first social relation graph according to the call data, wherein the first social relation graph takes a user as a vertex, and an edge is established between two vertices with the call data; updating the first social relation graph according to the personnel relation to obtain a second social relation graph; deleting the non-resident matched edges in the second social relationship graph to obtain a multi-user social relationship graph; the multi-user social relationship graph comprises multi-user social relationship graphs of all center points taking all divided areas as center points in advance;
the image recognition module is used for acquiring a front face image of the registered person and recognizing registered person identity information of the registered person according to the front face image;
The identity judging module is used for judging whether the registered person and the doctor are the same person or not according to the registered person identity information and the second identity information of the doctor;
The map query module is used for querying the multi-user social relationship map according to the identity information of the registered person when the registered person and the doctor are determined not to be the same person, and determining whether the registered person and the doctor are related persons according to a query result;
The registration list generation module is used for generating a registration list for the doctor when the registration person and the doctor person are determined to be related according to the query result;
When the registered person and the patient are the same person, identifying the pain level of the registered person according to the front face image; generating a registration order for the registered person according to the pain level; the identifying the pain level of the registered person according to the frontal face image comprises: preprocessing the front face image; converting the preprocessed front face image into a gray image; inputting the gray level image into a pre-trained pain level identification model for identification to obtain a pain level; obtaining the registered person from the pain class; calculating a difference between the self-pain level and the pain level; and when the difference value is greater than or equal to a preset difference value threshold value, retraining the pain level identification model according to the front face image and the pain level.
8. A computer device, the computer device comprising:
A memory for storing a computer program;
a processor for implementing a face recognition based anti-counterfeiting registration method according to any one of claims 1 to 6 when executing the computer program.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a face recognition based anti-impersonation method according to any of claims 1 to 6.
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| CN113628735A (en) * | 2021-08-11 | 2021-11-09 | 中国医学科学院阜外医院 | Online appointment registration method and device based on neural network |
| CN113688784B (en) * | 2021-09-10 | 2024-05-14 | 平安医疗健康管理股份有限公司 | Face recognition-based medical insurance card embezzlement risk recognition method and related equipment thereof |
| CN115497615B (en) * | 2022-10-24 | 2023-09-01 | 北京亿家老小科技有限公司 | Remote medical method and system |
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