WO2021196831A1 - Procédé de vérification de données fondé sur des informations vidéo, dispositif, et support de stockage - Google Patents

Procédé de vérification de données fondé sur des informations vidéo, dispositif, et support de stockage Download PDF

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Publication number
WO2021196831A1
WO2021196831A1 PCT/CN2021/071987 CN2021071987W WO2021196831A1 WO 2021196831 A1 WO2021196831 A1 WO 2021196831A1 CN 2021071987 W CN2021071987 W CN 2021071987W WO 2021196831 A1 WO2021196831 A1 WO 2021196831A1
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subject
verification
video information
actor
heart rate
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PCT/CN2021/071987
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English (en)
Chinese (zh)
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李伟
赵之砚
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深圳壹账通智能科技有限公司
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Publication of WO2021196831A1 publication Critical patent/WO2021196831A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/018—Certifying business or products
    • G06Q30/0185—Product, service or business identity fraud
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • 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
    • G06V40/174—Facial expression recognition
    • 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
    • G06V40/20—Movements or behaviour, e.g. gesture recognition

Definitions

  • This application relates to the field of video processing technology, and in particular to a data verification method, device and storage medium based on video information.
  • the main filling mode of the health notice is: operate in the APP or official account or H5 link, and manually select the answer items for the content in the health notice.
  • the insured (or the insured) will be required to truthfully fill in the health notice when purchasing insurance. Insured persons who are not insured with a healthy body may have to pay more premiums or be refused insurance. Therefore, in the actual operation process, there will be the following situations in which the health notice is not truthfully filled in:
  • the insurance broker fills in on behalf of the insured person.
  • the inventor realized that for the underwriters unable to judge the sub-insurance, the insurance policy may be "postponed" by the insurance company to review the relevant information of the customer, re-fill the notice, and re-review, which reduces the work efficiency of the insurance company.
  • the purpose of this application is to provide a data verification method, device and storage medium based on video information. It can be used in the entire process of responding to the health notice, using image and voice detection and recognition technology to prevent malicious insurance from fraudulent insurance, and pre-position the underwriting work, which can save a lot of manpower and material resources to do inspections, underwriting, etc.
  • a data verification method based on video information includes the following steps:
  • S110 Collect data to be verified, where the data to be verified includes first video information including an acting subject;
  • S120 According to the first video information, perform real person identification and first identity verification on the actors in the data to be verified to determine whether the actors are real people and whether they are consistent with the pre-stored ID photos ; If the subject is a real person and is consistent with the pre-stored ID photo, then go to S130; if the subject is not a real person and/or is inconsistent with the pre-stored ID photo, then go to S110 ;
  • S130 Push at least one verification question to the actor in turn, and collect second video information when the actor answers the verification question;
  • S140 According to the second video information, perform second identity verification and micro-expression analysis on the actor respectively to determine whether the actor is consistent with the actor in the first video, and determine the behavior Whether the subject has cheated in the process of answering the verification question; if the subject is inconsistent with the subject in the first video, stop pushing new verification questions to the subject; if the subject is If the actors in the first video are the same, but the micro-expression analyzes that the actors have deceptive behaviors in the process of answering the verification question, the verification question is marked as an abnormal question.
  • a data verification system based on video information including:
  • the first video acquisition unit is configured to collect data to be verified, where the data to be verified includes first video information including an action subject;
  • the real person recognition and first identity verification unit is configured to perform real person recognition and first identity verification on the actors in the data to be verified according to the first video information to determine whether the actors are real people , And whether it is consistent with the pre-stored ID photo; if the actor is a real person and is consistent with the pre-stored ID photo, then the second video collection and verification question push is performed; if the actor is not a real person , And/or is inconsistent with the pre-stored ID photo, then restart the first video capture unit;
  • a verification question push and second video acquisition unit configured to push at least one verification question to the actor in turn, and collect second video information when the actor answers the verification question;
  • the deceptive behavior determination unit is configured to perform second identity verification and micro-expression analysis on the behavior subject according to the second video information to determine whether the behavior subject is consistent with the behavior subject in the first video, And determine whether the actor has cheated in the process of answering the verification question; if the actor is inconsistent with the actor in the first video, stop pushing new verification questions to the actor; if The actor is consistent with the actor in the first video, but the micro-expression analyzes that the actor has cheating in the process of answering the verification question, and the verification question is marked as an abnormal question.
  • an electronic device comprising: a memory and a processor, and a computer program is stored in the memory.
  • the computer program is executed by the processor, the following steps are implemented: S110: collecting data to be verified , The data to be verified includes the first video information including the behavior subject;
  • S120 According to the first video information, perform real person identification and first identity verification on the actors in the data to be verified to determine whether the actors are real people and whether they are consistent with the pre-stored ID photos ; If the subject is a real person and is consistent with the pre-stored ID photo, then go to S130; if the subject is not a real person and/or is inconsistent with the pre-stored ID photo, then go to S110 ;
  • S130 Push at least one verification question to the actor in turn, and collect second video information when the actor answers the verification question;
  • S140 According to the second video information, perform second identity verification and micro-expression analysis on the actor respectively to determine whether the actor is consistent with the actor in the first video, and determine the behavior Whether the subject has cheated in the process of answering the verification question; if the subject is inconsistent with the subject in the first video, stop pushing new verification questions to the subject; if the subject is If the actors in the first video are the same, but the micro-expression analyzes that the actors have deceptive behaviors in the process of answering the verification question, the verification question is marked as an abnormal question.
  • a computer-readable storage medium in which a data verification program based on video information is stored, and when the data verification program based on video information is executed by a processor , The following steps are implemented: S110: Collect data to be verified, where the data to be verified includes the first video information including the behavior subject;
  • S120 According to the first video information, perform real person identification and first identity verification on the actors in the data to be verified to determine whether the actors are real people and whether they are consistent with the pre-stored ID photos ; If the subject is a real person and is consistent with the pre-stored ID photo, then go to S130; if the subject is not a real person and/or is inconsistent with the pre-stored ID photo, then go to S110 ;
  • S130 Push at least one verification question to the actor in turn, and collect second video information when the actor answers the verification question;
  • S140 According to the second video information, perform second identity verification and micro-expression analysis on the actor respectively to determine whether the actor is consistent with the actor in the first video, and determine the behavior Whether the subject has cheated in the process of answering the verification question; if the subject is inconsistent with the subject in the first video, stop pushing new verification questions to the subject; if the subject is If the actors in the first video are the same, but the micro-expression analyzes that the actors have deceptive behaviors in the process of answering the verification question, the verification question is marked as an abnormal question.
  • FIG. 1 is a flowchart of a data verification method based on video information according to Embodiment 1 of the present application;
  • FIG. 2 is a schematic diagram of the logical structure of a data verification system based on video information according to Embodiment 2 of the present application;
  • FIG. 3 is a schematic diagram of a logical structure of an electronic device according to Embodiment 3 of the present application.
  • Silent multi-frame live detection model Based on the latest deep convolutional neural network, combined with hundreds of millions of real face data and non-real face data training. Non-real face pictures have certain characteristics, such as moiré, picture reflections, distortions, abnormal backgrounds, etc.
  • the silent multi-frame live detection model detects multiple pictures from the video to determine whether it is taken by a real person.
  • RPPG heartbeat detection Remote Photoplethysmography (RPPG) uses the reflected ambient light to measure the subtle brightness changes of the skin. The subtle changes in skin brightness are caused by the blood flow caused by the beating heart. Generally, we can get a signal similar to BVP (blood volume pulse) through RPPG, and the heart rate can be predicted through this signal.
  • BVP blood volume pulse
  • FFmpeg is an open source computer program that can be used to record, convert digital audio and video, and convert them into streams. It provides a complete solution for recording, converting, and streaming audio and video.
  • Fig. 1 is a flowchart of a data verification method based on video information according to Embodiment 1 of the present application.
  • the method for data verification based on video information provided in this embodiment includes the following steps:
  • S110 Collect the data to be verified, and the data to be verified includes the first video information including the actors;
  • the actor in the data to be verified may be the respondent who will reply to the health notice.
  • a front camera is set on the display screen, and the front camera shoots the first video information of the respondent, which can be displayed on the display screen.
  • the first video information is used for real person identification and identity verification of the respondent.
  • S120 According to the first video information, perform real person identification and first identity verification on the respondent to determine whether the respondent is a real person and whether it is consistent with the pre-stored ID photo; if the respondent is a real person and is consistent with the pre-stored If the ID photo is consistent, go to s130; if the respondent is not a real person, and/or is inconsistent with the pre-stored ID photo, go to s110;
  • this step it is judged whether it is a real person who is recording the video, and whether it is the person on the submitted ID card who is recording the video. If the two conditions are met at the same time, the following health question answering stage can be carried out. If one of the conditions is not met, it means that the respondent has a tendency to cheat insurance, and the following health questions will no longer be displayed, stop the question and answer of the health notice, and record the first video of other respondents.
  • S130 Push verification questions to the answerer in turn, and collect the second video information when the answerer answers the verification question;
  • the verification question can be a single health question, a single health question is displayed on the display screen one by one, and the second video information when the respondent answers the single health question is collected and displayed on the display screen.
  • the display screen only displays one health question.
  • the front camera collects the video of the respondent as the second video information.
  • the second video information and this health question are displayed on the display at the same time. After the respondent has answered this health question, the next health question will be displayed.
  • S140 According to the second video information, perform the second identity verification and micro-expression analysis on the respondent to determine whether the respondent is consistent with the respondent in the first video, and whether the respondent cheated in the process of answering the health question Behavior; if the answerer is inconsistent with the answerer in the first video, stop pushing new verification questions to the answerer; if the answerer is consistent with the answerer in the first video, but the micro-expression analysis answerer is answering healthy If there is deceptive behavior during the problem process, the health problem in this article will be marked as an abnormal problem.
  • a corresponding second video information is collected, which is used to judge whether the respondent has cheated when answering each health question, and record it and save it. It is convenient for the auditor to review the health notice in a targeted manner. After all the questions have been answered, save all the second videos, so that the video files recorded by the respondent when answering the questions can be kept for a long time, providing powerful supporting evidence for possible disputes in the future.
  • step S120 Specifically, in step S120:
  • the multimedia video processing tool FFmpeg is used to separate the image information and the voice information of the first video information to obtain the first image information and the first voice information.
  • real person recognition is performed on the answerer to determine whether the answerer is a real person, including the following process:
  • Intercept at least 20,000 first face pictures from the first image information through frame extraction technology, and calculate the first face picture according to the silent multi-frame liveness detection model to determine whether it is a real person shooting, and the silent multi-frame liveness detection
  • This method can effectively prevent other people from using paper photos, mobile phone photos, and mobile phone videos to collect faces, and truly achieve real face collection.
  • the obtained heart rate is within the set range, calculate the average heart rate fluctuation value according to each heart rate value. If the heart rate is less than the average heart rate fluctuation value, and the result calculated according to the silent multi-frame live detection model is a real person, It is judged that the answerer is a real person, and the real person is recognized and passed;
  • the range of heart rate setting can be: (50-160) beats/min.
  • the calculation process of the average fluctuation value of heart rate includes:
  • the first face pictures into M groups according to the time sequence of the interception. Each group includes N first face pictures.
  • the maximum heart rate value in each group is subtracted from the minimum heart rate value to obtain the heart rate difference.
  • the heart rate difference of each group is added and the average value is calculated, and the average value is the average fluctuation value of the heart rate. Calculated as follows:
  • M is the number of shooting groups, M can be greater than 10,000, and each group has N face pictures; H 1 , H 2 , H 3 ,..., H N is a group of intercepting each face picture
  • the heart rate value obtained through the RPPG heartbeat detection at time; A is the average heart rate fluctuation value.
  • Real person recognition adopts the combination of heartbeat detection and silent multi-frame live detection model at the same time, which increases the reliability.
  • real person recognition is performed on the respondent to determine whether the respondent is a real person.
  • Voice recognition and lip language recognition can also be used, including:
  • the first identity verification is performed on the respondent to determine whether the respondent is consistent with the pre-stored ID photo, including the following process:
  • the face image detection algorithm model can be used to detect the quality of each first face picture, or a few pictures can be randomly selected, and at least one first face picture meeting the preset quality conditions can be selected and stored as a standard face picture ; Compare the standard face picture with the ID photo stored by the respondent to obtain the similarity between the standard face picture and the ID photo. If the similarity is higher than the preset similarity of the ID, the first identity verification is passed.
  • the face image detection algorithm model can detect the entire face image.
  • the preset quality conditions can be: whether the face features are available, whether the proportion of the face meets the requirements (20%-70%), and whether the overall image pixels meet the setting Require.
  • the purpose of detection is to determine whether the extracted first face image meets the conditions for comparison, and to provide a higher-quality image as a standard face image.
  • this step it is judged whether it is the person on the ID card who is recording the video, and the health question will be asked. If the person on the ID card is recording the video and the real person is recording the video, then the following health question answering stage can be carried out.
  • step S130 while the display screen displays the health problem, the voice announcement of the displayed health problem can also be performed automatically through the speaker. Respondents can listen to every health question, avoiding that some respondents do not read the question carefully and answer at will.
  • step S140 Specifically, in step S140:
  • the multimedia video processing tool FFmpeg is used to separate the image information and the voice information of the second video information to obtain the second image information and the second voice information.
  • the second identity verification and micro-expression analysis are performed on the respondent to determine whether the respondent is consistent with the respondent in the first video, and to determine whether the respondent is cheating in the process of answering the health question. It includes the following steps:
  • the second image information in the second video information is framed within a set time to obtain a second face picture, and the second face picture is compared with the standard face image obtained in s120 for second identity verification, If the comparison results are different, the respondent has cheated during the course of answering the health question, stop pushing the health question to the respondent, and stop responding to the respondent’s health notice. If the comparison result is the same, the answerer is the same as the answerer in the first video, and there is no change in the middle, and the health notice will not stop responding.
  • input the second face image into the facial expression classification model based on convolutional neural network for micro-expression analysis to determine whether the respondent has cheated in answering health questions. If the second face image corresponds to the micro-expression analysis result If it shows deceptive behavior, it has a tendency to deceive. Mark this health problem as an abnormal problem and save it.
  • Respondents answer a health question. If there is a violation of ordinary people’s answering habits, it will be marked as an abnormal question. After all the questions and answers of the entire health notice are completed, the auditor will record the abnormality according to which health question has been recorded before. Knowing that the person's emotional and psychological changes are large, there is a possibility of falsification in the answer to this health question. After all the questions and answers of the entire health notice are completed, the auditor will record the abnormality based on which health problem has been recorded before, and know at which link there is a tendency to deceive. If there is a substitution response, the response to the health notice will be stopped. By checking the recorded abnormal questions, the auditor can easily figure out whether the respondent has false answers when answering each health notification question. If there are false answers, they can conduct data review and so on.
  • the answer to a single health question stored in the database includes multiple answers that can be insured for that health question.
  • the matching rate can indicate the similarity between the answer of the answerer and the answer stored in the database. A high similarity indicates that the answer of the answerer meets the insurance conditions of the health question, and a low similarity indicates that the answer of the answerer needs to be further reviewed by the insurance reviewer.
  • the insurance auditor can determine which insurance condition of the customer needs to be further audited according to the matching rate of each stored health question answer, which improves the auditor's work efficiency and reduces the work intensity.
  • Lip recognition can also be used, which specifically includes the following process: lip recognition is performed on the second image information, and lips are obtained through the face recognition model Then, the lip language recognition algorithm model established by the deep learning neural network matches the lip language model with the lip language model of the single health question answer stored in the database, and obtains the lip language model of the lip motion and answer through the face recognition model Match rate, save lip action and match rate.
  • the lip language recognition algorithm model mainly uses deep learning model algorithms based on time series recognition such as RNN (recurrent neural network) + LSTM (long short-term memory network).
  • the health question to be answered and the video and audio of the respondent recorded by the camera are displayed on the screen at the same time.
  • the respondent reads and listens to the health question
  • the respondent’s face, voice, lip print, expression and other information are collected for real person recognition, identity authentication, confirmation of answer content, and recognition of fraudulent responses, which improves insurance and verification. Guaranteed efficiency.
  • Fig. 2 is a flowchart of a data verification method based on video information according to Embodiment 1 of the present application.
  • a data verification system based on video information includes: a first video collection unit 201, a real person recognition and first identity verification unit 202, a verification question push and a second video collection unit 203 And the deceptive behavior judging unit 204.
  • the first video collection unit 201 is configured to collect data to be verified, and the data to be verified includes first video information including an action subject;
  • the real person recognition and first identity verification unit 202 is configured to perform real person recognition and first identity verification on the actors in the data to be verified according to the first video information, to determine whether the actors are real persons and whether they are related to pre-stored certificates If the actor is a real person and it is consistent with the pre-stored ID photo, then the second video collection and verification problem will be pushed; if the actor is not a real person, and/or is inconsistent with the pre-stored ID photo, it will be renewed Perform the first video capture unit;
  • the verification question push and second video collection unit 203 is configured to push at least one verification question to the actor in turn, and collect the second video information when the actor answers the verification question;
  • the deceptive behavior judging unit 204 is configured to perform second identity verification and micro-expression analysis on the actor according to the second video information, determine whether the actor is consistent with the actor in the first video, and determine whether the actor is answering the verification question Whether there is deceptive behavior in the process; if the actor is inconsistent with the actor in the first video, stop pushing new verification questions to the actor; if the actor is consistent with the actor in the first video, but the micro-expression analysis of the actor If there is fraud in answering the verification question, the verification question will be marked as an abnormal question.
  • FIG. 3 is a schematic diagram of a logical structure of an electronic device according to Embodiment 3 of the present application.
  • an electronic device 1 includes a memory 3 and a processor 2.
  • a computer program 4 is stored in the memory, and the computer program 4 is executed by the processor 3 as follows:
  • S110 Collect data to be verified, where the data to be verified includes first video information including an acting subject;
  • S120 According to the first video information, perform real person identification and first identity verification on the actors in the data to be verified to determine whether the actors are real people and whether they are consistent with the pre-stored ID photos ; If the subject is a real person and is consistent with the pre-stored ID photo, then go to S130; if the subject is not a real person and/or is inconsistent with the pre-stored ID photo, then go to S110 ;
  • S130 Push at least one verification question to the actor in turn, and collect second video information when the actor answers the verification question;
  • S140 According to the second video information, perform second identity verification and micro-expression analysis on the actor respectively to determine whether the actor is consistent with the actor in the first video, and determine the behavior Whether the subject has cheated in the process of answering the verification question; if the subject is inconsistent with the subject in the first video, stop pushing new verification questions to the subject; if the subject is If the actors in the first video are the same, but the micro-expression analyzes that the actors have deceptive behaviors in the process of answering the verification question, the verification question is marked as an abnormal question.
  • a computer-readable storage medium including a data verification program based on video information.
  • the data verification program based on video information is executed by a processor, the following steps are implemented:
  • S110 Collect data to be verified, where the data to be verified includes first video information including an acting subject;
  • S120 According to the first video information, perform real person identification and first identity verification on the actors in the data to be verified to determine whether the actors are real people and whether they are consistent with the pre-stored ID photos ; If the subject is a real person and is consistent with the pre-stored ID photo, then go to S130; if the subject is not a real person and/or is inconsistent with the pre-stored ID photo, then go to S110 ;
  • S130 Push at least one verification question to the actor in turn, and collect second video information when the actor answers the verification question;
  • S140 According to the second video information, perform second identity verification and micro-expression analysis on the actor respectively to determine whether the actor is consistent with the actor in the first video, and determine the behavior Whether the subject has cheated in the process of answering the verification question; if the subject is inconsistent with the subject in the first video, stop pushing new verification questions to the subject; if the subject is If the actors in the first video are the same, but the micro-expression analyzes that the actors have deceptive behaviors in the process of answering the verification question, the verification question is marked as an abnormal question.
  • the computer-readable storage medium may be non-volatile or volatile.

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Abstract

L'invention concerne un procédé de vérification de données fondé sur des informations vidéo, dispositif, et support de stockage, se rapportant au domaine technique du traitement vocal. Le procédé consiste : à collecter des données à vérifier, lesdites données comprenant des premières informations vidéo contenant un sujet d'action (S110) ; à effectuer respectivement une identification de personne réelle et une première vérification d'identité du sujet d'action dans lesdites données en fonction des premières informations vidéo, de manière à déterminer si le sujet d'action est une personne réelle et si le sujet d'action est conforme à une photographie d'identification pré-stockée (S120) ; si le sujet d'action est une personne réelle et est conforme à la photographie d'identification pré-stockée, à pousser un problème de vérification vers le sujet d'action, et à collecter des secondes informations vidéo lorsque le sujet d'action répond au problème de vérification (S130) ; et à effectuer respectivement une seconde vérification d'identité et une analyse de micro-expressions sur le sujet d'action en fonction des secondes informations vidéo, de manière à déterminer si le sujet d'action est conforme au sujet d'action dans la première vidéo et si le sujet d'action a un comportement d'usurpation dans le processus de réponse au problème de vérification (S140). Le procédé détermine, à l'aide d'une technologie de reconnaissance d'image et de voix, si une personne interrogée a un comportement d'usurpation lors de la réponse à l'avis de santé.
PCT/CN2021/071987 2020-03-30 2021-01-15 Procédé de vérification de données fondé sur des informations vidéo, dispositif, et support de stockage WO2021196831A1 (fr)

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CN202010236490.0 2020-03-30
CN202010236490.0A CN111553189A (zh) 2020-03-30 2020-03-30 基于视频信息的数据验证方法、装置及存储介质

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