WO2019085403A1 - Procédé de comparaison intelligente de reconnaissance faciale, dispositif électronique et support d'informations lisible par ordinateur - Google Patents

Procédé de comparaison intelligente de reconnaissance faciale, dispositif électronique et support d'informations lisible par ordinateur Download PDF

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Publication number
WO2019085403A1
WO2019085403A1 PCT/CN2018/083071 CN2018083071W WO2019085403A1 WO 2019085403 A1 WO2019085403 A1 WO 2019085403A1 CN 2018083071 W CN2018083071 W CN 2018083071W WO 2019085403 A1 WO2019085403 A1 WO 2019085403A1
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Prior art keywords
comparison
face
card
face recognition
user
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PCT/CN2018/083071
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English (en)
Chinese (zh)
Inventor
凌永辉
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平安科技（深圳）有限公司
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Publication of WO2019085403A1 publication Critical patent/WO2019085403A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • 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
    • G06V40/178—Human faces, e.g. facial parts, sketches or expressions estimating age from face image; using age information for improving recognition

Definitions

  • the present application relates to an authentication method, and in particular, to an identification method, an electronic device, and a computer readable storage medium.
  • the face recognition technology is used to verify the identity of the user, and when the face recognition is performed, a fixed face recognition comparison threshold is adopted for all the crowds, which makes it difficult for a person with a large change in appearance to handle the business through face recognition.
  • the success rate of face matching in a specific group is reduced, and customers are prone to loss.
  • the purpose of the present application is to provide a face recognition intelligent comparison method, an electronic device, and a computer readable storage medium, thereby further overcoming the problems existing in the prior art to some extent.
  • the present application provides a method for intelligently comparing face recognition, including the following steps:
  • Step 01 Collect ID information of the user to be identified.
  • Step 02 Determine whether the ID card is valid, if it is valid, proceed to step S3, and if invalid, the prompt is invalid.
  • Step 03 Perform face recognition comparison to obtain a first comparison threshold.
  • Step 04 Determine the population to which the user belongs according to the ID card information, and obtain a face recognition comparison reference threshold value of the belonging group.
  • Step 05 Determine whether the first comparison threshold is greater than or equal to the face recognition comparison reference threshold, and if yes, prompt the recognition to pass, if otherwise, the recognition fails.
  • the present application further provides an electronic device including a memory and a processor for storing a face recognition intelligent comparison system executed by a processor, the face recognition intelligent comparison system comprising:
  • the identity information collection module is configured to collect the ID number, avatar and age range information of the user ID card.
  • the ID card validity judging module is configured to compare the collected ID card information with the information in the third-party ID card information network to determine whether the ID card is valid.
  • the face recognition module is configured to compare the collected scene face photos with the ID card avatar photos, and give a similarity comparison value, that is, a first comparison threshold.
  • the reference threshold judging module is configured to determine, according to the extracted user age segment information, a population of the user and a face recognition comparison reference threshold of the crowd.
  • the threshold comparison module is configured to compare the first alignment threshold with the face recognition comparison reference threshold and give a comparison result.
  • the present application further provides a computer readable storage medium having a breakpoint data follow-up system stored therein, the breakpoint data follow-up system being executable by at least one processor To achieve the following steps:
  • Step 01 collecting identity card information of the user to be identified
  • Step 02 Determine whether the ID card is valid, if yes, proceed to step S3, and if invalid, the prompt is invalid;
  • Step 03 Perform face recognition comparison to obtain a first comparison threshold
  • Step 04 Determine, according to the ID card information, the user belonging to the group, and obtain a reference threshold for the face recognition comparison of the belonging group;
  • Step 05 Determine whether the first comparison threshold is greater than or equal to the face recognition comparison reference threshold, and if yes, prompt the recognition to pass, if otherwise, the recognition fails.
  • the solution avoids the decrease of matching success rate caused by using a uniform comparison threshold, provides recognition success rate of different groups, improves business processing efficiency and customer satisfaction. degree.
  • FIG. 1 is a flow chart showing an embodiment of the present applicant's face recognition intelligent comparison method.
  • FIG. 2 is a flow chart showing still another embodiment of the present applicant's face recognition intelligent comparison method.
  • FIG. 3 is a schematic diagram of a program module of an embodiment of the present applicant's face recognition intelligent comparison system.
  • FIG. 4 is a schematic diagram showing a program module of still another embodiment of the present applicant's face recognition intelligent comparison system.
  • FIG. 5 is a schematic diagram showing a program module of still another embodiment of the present applicant's face recognition intelligent comparison system.
  • FIG. 6 is a schematic diagram showing the hardware architecture of an embodiment of an electronic device of the present application.
  • a method for intelligently matching a face recognition which includes the following steps:
  • Step 01 Collect ID information of the user to be identified.
  • the ID card number, the avatar photo, and the age segment information of the user are collected by the ID card collector.
  • the ID card number is used to match and validate the information in the third-party identity information network, and is used to extract the age information of the user, and the avatar photo is used for face recognition verification.
  • step 02 it is judged whether the ID card is valid, if it is valid, the process proceeds to step 03, and if it is invalid, the prompt is invalid.
  • the collected ID information is compared with the data of the third-party identity information network to determine whether the ID card is valid, and whether it is still within the validity period. If it is determined to be invalid or exceeds the validity period, the direct feedback is invalid or the overdue prompt is Do not follow the next steps or prompt to go to the manual counter; if it is judged to be valid, perform step 03, this step can improve the efficiency of the query, eliminating the need to spend more time on the authentication and ultimately failing due to the expiration of the ID card Inquire.
  • the third-party identity information network may be a public security information inquiry network, and the public information inquiry network is used to query and obtain the user's avatar photo and identity information through a dedicated interface.
  • step 03 a face recognition comparison is performed to obtain a first comparison threshold.
  • the camera is opened to take a photo of the face, and the user's face photo collected on the spot is compared with the avatar photo on the ID card, wherein the face recognition comparison includes face collection and face features. Positioning, face feature extraction and face feature similarity comparison.
  • the face collection includes marking the face coordinates and detecting whether there is a human face, evaluating the shooting quality, and screenshotting the face image. Specifically, including marking the face coordinates after opening the camera and detecting whether there is a face, evaluating the shooting quality, and screening the face. image. It is detected whether a human face can judge whether it has a positive facial features and has a complete facial contour according to the coordinates of the hit and the pre-existing range of facial features.
  • Evaluating the photographing quality may include a head angle evaluation, a brightness evaluation, and a motion blur evaluation
  • the head angle evaluation includes determining whether the head is up and down, for example, within 15 degrees, the left and right declination is within, for example, 15 degrees, and the rotational declination is, for example, Within 20°, if it is consistent, it is considered to meet the head angle assessment
  • the brightness evaluation includes determining whether the brightness is within, for example, [80,200], if it is met, it is considered to meet the brightness evaluation
  • the dynamic fuzzy evaluation includes judging the fuzzy value. Whether it is less than 0.2, for example, if it is met, it is considered to be in compliance with the dynamic fuzzy assessment.
  • evaluating the quality of the shot may also include determining whether the user wears thick-rimmed glasses, sunglasses, or whether the hair blocks the ears or other facial features.
  • the facial feature localization includes positioning a plurality of features of the human face including the organ, including positioning a plurality of features of the human face including the eyebrows, the eyes, the nose, the mouth, and the like.
  • the facial feature extraction includes extracting a plurality of feature information of each feature according to a preset extraction rule.
  • the face feature similarity comparison includes comparing the extracted feature information with the feature information of the ID card avatar photo and obtaining a first comparison threshold, wherein the face feature may include a length and a slope.
  • the parameters such as the gradation difference represent the three-dimensional size, the oblique direction, the distance from other parts, and the like, and the face feature may be a set of feature information.
  • the facial feature similarity comparison may be to compare the two sets of feature information one by one, and define each feature information to have a certain weight, for example, the weight of the important feature information, and the weight of the secondary feature information is relatively small, and may also be defined. Some feature information is a necessary condition for judging that it must be consistent.
  • the photo is detextured after the photo of the ID card is obtained to improve the recognition effect.
  • the step of adjusting the camera angle and the shooting parameters according to the user information such as age, gender, and region in the identity information before the face collecting step is performed, so as to facilitate shooting of users with different features. Verify the required photos, reduce the false positive rate and increase the pass rate.
  • Step 04 Determine the population to which the user belongs according to the ID card information, and obtain a face recognition comparison reference threshold value of the belonging group.
  • the age of the user is extracted from the collected ID number, the age of the user is compared with the age group of the preset crowd, the population to which the user belongs is determined, and the reference threshold of the face recognition of the user is obtained according to the belonging group. .
  • the population is divided into three categories according to gender and age: A, B, and C.
  • Class A is a male with age ⁇ 50
  • class B is a woman with age ⁇ 50
  • class C is an age group with age ⁇ 50.
  • the face recognition comparison threshold of the class A population is 66
  • the face recognition comparison threshold value of the B group is 60
  • the face recognition comparison threshold of the C group is 55.
  • the threshold is dynamically adjustable. The face recognition score of a specific group will be counted again for a period of time, and then the face recognition comparison reference threshold of each group is automatically adjusted according to the newly obtained value.
  • the baseline threshold for each population is set based on historical alignment data, such as statistically identifying the face recognition scores for the elderly, and then setting a comparison reference threshold based on statistical scores and business needs. According to the extracted age information of the on-site user, it is determined which one of the categories A, B, and C the user belongs to, and the reference threshold of the user face recognition comparison is determined according to the belonging group.
  • the corresponding face recognition reference threshold is set by a specific group, which improves the success rate of the population identification matching with the change of the age with the increase of the age, and improves the number of customers and the efficiency of business processing.
  • Step 05 Determine whether the first comparison threshold is greater than or equal to the face recognition comparison reference threshold, and if yes, prompt the recognition to pass, if otherwise, the recognition fails.
  • the first comparison threshold obtained in the face recognition comparison step is compared with the reference threshold. If the first comparison threshold is greater than or equal to the reference threshold, the user identity verification is passed, and the user is prompted to pass. After entering the next business process, if the first comparison threshold is less than the reference threshold, the authentication fails, prompting the user to identify the failure.
  • FIG. 2 another method for intelligently matching face recognition is shown, which includes the following steps:
  • Step 01 Collect ID information of the user to be identified.
  • the ID card number, the avatar photo, and the age segment information of the user are collected by the ID card collector.
  • the ID card number is used to match and validate the information in the third-party identity information network, and is used to extract the age information of the user, and the avatar photo is used for face recognition verification.
  • step 02 it is judged whether the ID card is valid, if it is valid, the process proceeds to step 03, and if it is invalid, the prompt is invalid.
  • the collected ID information is compared with the data of the third-party identity information network to determine whether the ID card is valid, and whether it is still within the validity period. If it is determined to be invalid or exceeds the validity period, the direct feedback is invalid or the overdue prompt is Do not follow the next steps or prompt to go to the manual counter; if it is judged to be valid, perform step 03, this step can improve the efficiency of the query, eliminating the need to spend more time on the authentication and ultimately failing due to the expiration of the ID card Inquire.
  • the third-party identity information network may be a public security information inquiry network, and the public information inquiry network is used to query and obtain the user's avatar photo and identity information through a dedicated interface.
  • step 03 a face recognition comparison is performed to obtain a first comparison threshold.
  • the camera is opened to take a photo of the face, and the user's face photo collected on the spot is compared with the avatar photo on the ID card, wherein the face recognition comparison includes face collection and face features. Positioning, face feature extraction and face feature similarity comparison.
  • the face collection includes marking the face coordinates and detecting whether there is a human face, evaluating the shooting quality, and screenshotting the face image. Specifically, including marking the face coordinates after opening the camera and detecting whether there is a face, evaluating the shooting quality, and screening the face. image. It is detected whether a human face can judge whether it has a positive facial features and has a complete facial contour according to the coordinates of the hit and the pre-existing range of facial features.
  • Evaluating the photographing quality may include a head angle evaluation, a brightness evaluation, and a motion blur evaluation
  • the head angle evaluation includes determining whether the head is up and down, for example, within 15 degrees, the left and right declination is within, for example, 15 degrees, and the rotational declination is, for example, Within 20°, if it is consistent, it is considered to meet the head angle assessment
  • the brightness evaluation includes determining whether the brightness is within, for example, [80,200], if it is met, it is considered to meet the brightness evaluation
  • the dynamic fuzzy evaluation includes judging the fuzzy value. Whether it is less than 0.2, for example, if it is met, it is considered to be in compliance with the dynamic fuzzy assessment.
  • evaluating the quality of the shot may also include determining whether the user wears thick-rimmed glasses, sunglasses, or whether the hair blocks the ears or other facial features.
  • the facial feature localization includes positioning a plurality of features of the human face including the organ, including positioning a plurality of features of the human face including the eyebrows, the eyes, the nose, the mouth, and the like.
  • the facial feature extraction includes extracting a plurality of feature information of each feature according to a preset extraction rule.
  • the face feature similarity comparison includes comparing the extracted feature information with the feature information of the ID card avatar photo and obtaining a first comparison threshold, wherein the face feature may include a length and a slope.
  • the parameters such as the gradation difference represent the three-dimensional size, the oblique direction, the distance from other parts, and the like, and the face feature may be a set of feature information.
  • the facial feature similarity comparison may be to compare the two sets of feature information one by one, and define each feature information to have a certain weight, for example, the weight of the important feature information, and the weight of the secondary feature information is relatively small, and may also be defined. Some feature information is a necessary condition for judging that it must be consistent.
  • the photo ie, the photo of the ID card in the public security system
  • the photo is detextured to improve the recognition effect.
  • the step of adjusting the camera angle and the shooting parameters according to the user information such as age, gender, and region in the identity information before the face collecting step is performed, so as to facilitate shooting of users with different features. Verify the required photos, reduce the false positive rate and increase the pass rate.
  • Step 04 Determine the population to which the user belongs according to the ID card information, and obtain a face recognition comparison reference threshold value of the belonging group.
  • the age of the user is extracted from the collected ID number, the age of the user is compared with the age group of the preset crowd, the population to which the user belongs is determined, and the reference threshold of the face recognition of the user is obtained according to the belonging group. .
  • the population is divided into three categories according to gender and age: A, B, and C.
  • Class A is a male with age ⁇ 50
  • class B is a woman with age ⁇ 50
  • class C is an age group with age ⁇ 50.
  • the face recognition comparison threshold of the class A population is 66
  • the face recognition comparison threshold value of the B group is 60
  • the face recognition comparison threshold of the C group is 55.
  • the threshold is dynamically adjustable. The face recognition score of a specific group will be counted again for a period of time, and then the face recognition comparison reference threshold of each group is automatically adjusted according to the newly obtained value.
  • the baseline threshold for each population is set based on historical alignment data, such as statistically identifying the face recognition scores for the elderly, and then setting a comparison reference threshold based on statistical scores and business needs. According to the extracted age information of the on-site user, it is determined which one of the categories A, B, and C the user belongs to, and the reference threshold of the user face recognition comparison is determined according to the belonging group.
  • the corresponding face recognition reference threshold is set by a specific group, which improves the success rate of the population identification matching with the change of the age with the increase of the age, and improves the number of customers and the efficiency of business processing.
  • Step 05 Determine whether the first comparison threshold is greater than or equal to the face recognition comparison reference threshold, and if yes, prompt the recognition to pass, if otherwise, the recognition fails.
  • the first comparison threshold obtained in the face recognition comparison step is compared with the reference threshold. If the first comparison threshold is greater than or equal to the reference threshold, the user identity verification is passed, and the user is prompted to pass and enter. In the next business process, if the first comparison threshold is less than the reference threshold, the authentication fails, prompting the user to identify the failure.
  • step 06 it is determined whether remote video assisted recognition is required, and if yes, the process proceeds to step 07, and if not, the process ends.
  • the user face recognition fails, prompting the user to identify the failure, prompting the user whether remote video assistance is required for recognition. If the user selects yes, the user proceeds to the next remote identification step. If the user selects no, the face recognition step ends. , the next step cannot be performed.
  • Step 07 Remote video assisted recognition, if the recognition is passed, the prompt recognition is passed, and if the recognition is not passed, the end is completed.
  • the remote agent sends a remote video request to the client.
  • the video recognition mode is enabled, and the remote end manually identifies whether the remote user is consistent with the ID card information. The user identification is confirmed to pass, and if the information is inconsistent, the user identification is not passed, and the identification step ends.
  • the remote video assisted recognition at the agent end is added, and the user identity is manually determined through the agent end.
  • the dual recognition method improves the success rate of the identity verification and improves the user experience.
  • a face recognition intelligent comparison system 20 is illustrated.
  • the face recognition intelligent comparison system 20 is divided into one or more program modules, and one or more program modules are stored.
  • the invention is implemented in a storage medium and executed by one or more processors.
  • a program module as used herein refers to a series of computer program instructions that are capable of performing a particular function. The following description will specifically describe the functions of each program module of this embodiment:
  • the ID card information collection module 201 is configured to collect the ID card number, the avatar, and the age group information of the user ID card.
  • the ID card validity judging module 202 is configured to compare the collected ID card information with the information in the third-party ID card information network to determine whether the ID card is valid.
  • the face recognition module 203 is configured to perform feature matching on the collected live face photo and the ID card avatar photo, and give a similarity comparison value, that is, a first comparison threshold.
  • the face recognition module 203 further includes a face collection sub-module 2031, a face feature locating sub-module 2032, a face feature extraction sub-module 2033, and a face similarity comparison sub-module 2034.
  • the reference threshold judging module 204 is configured to determine, according to the extracted user age segment information, a population to which the user belongs and a face recognition comparison reference threshold of the crowd.
  • the threshold comparison module 205 is configured to compare the first alignment threshold with the face recognition comparison reference threshold and give a comparison result.
  • FIG. 4 another face recognition intelligent comparison system 20 is illustrated.
  • the face recognition intelligent comparison system 20 is divided into one or more program modules, and one or more program modules are It is stored in a storage medium and executed by one or more processors to complete the application.
  • a program module as used herein refers to a series of computer program instructions that are capable of performing a particular function. The following description will specifically describe the functions of each program module of this embodiment:
  • the ID card information collection module 201 is configured to collect the ID card number, the avatar, and the age group information of the user ID card.
  • the ID card validity judging module 202 is configured to compare the collected ID card information with the information in the third-party ID card information network to determine whether the ID card is valid.
  • the face recognition module 203 is configured to perform feature matching on the collected live face photo and the ID card avatar photo, and give a similarity comparison value, that is, a first comparison threshold.
  • the face recognition module 203 further includes a face collection sub-module 2031, a face feature locating sub-module 2032, a face feature extraction sub-module 2033, and a face similarity comparison sub-module 2034.
  • the reference threshold judging module 204 is configured to determine, according to the extracted user age segment information, a population to which the user belongs and a face recognition comparison reference threshold of the crowd.
  • the threshold comparison module 205 is configured to compare the first comparison threshold with the face recognition comparison reference threshold and give a comparison result.
  • the secondary nucleus determining module 206 is configured to determine whether the user who does not pass the face recognition needs remote video assisted identification to verify the identity.
  • the remote video identification module 207 is configured to perform remote identification and authentication on a user who needs to perform remote video assistance identification.
  • the embodiment provides an electronic device. It is a schematic diagram of the hardware architecture of an embodiment of the electronic device of the present application.
  • the electronic device 2 is an apparatus capable of automatically performing numerical calculation and/or information processing in accordance with an instruction set or stored in advance.
  • it can be a smartphone, a tablet, a laptop, a desktop computer, a rack server, a blade server, a tower server, or a rack server (including a stand-alone server, or a server cluster composed of multiple servers).
  • the electronic device 2 includes at least, but not limited to, a memory 21, a processor 22, a network interface 23, an ID card collector 24, a camera 25, and a face recognition intelligent comparison.
  • System 20 among them:
  • the memory 21 includes at least one type of computer readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), Static Random Access Memory (SRAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, and the like.
  • the memory 21 may be an internal storage module of the electronic device 2, such as a hard disk or a memory of the electronic device 2.
  • the memory 21 may also be an external storage device of the electronic device 2, such as a plug-in hard disk equipped on the electronic device 2, a smart memory card (SMC), and a secure digital device. (Secure Digital, SD) card, flash card, etc.
  • the memory 21 can also include both the internal storage module of the electronic device 2 and its external storage device.
  • the memory 21 is generally used to store an operating system installed in the electronic device 2 and various types of application software, such as program codes of the face recognition intelligent comparison system 20. Further, the memory 21 can also be used to temporarily store various types of data that have been output or are to be output.
  • the processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments.
  • the processor 22 is typically used to control the overall operation of the electronic device 2, such as performing control and processing associated with data interaction or communication with the electronic device 2.
  • the processor 22 is configured to run program code or process data stored in the memory 21, such as running the face recognition intelligent comparison system 20 and the like.
  • the network interface 23 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the electronic device 2 and other electronic devices.
  • the network interface 23 is configured to connect the electronic device 2 to an external terminal through a network, establish a data transmission channel, a communication connection, and the like between the electronic device 2 and an external terminal.
  • the network may be an intranet, an Internet, a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, or a 5G network.
  • Wireless or wired networks such as network, Bluetooth, Wi-Fi, etc.
  • Figure 5 only shows the electronic device with components 21-25, but it should be understood that not all illustrated components may be implemented and that more or fewer components may be implemented instead.
  • the face recognition intelligent comparison system 20 stored in the memory 21 may also be divided into one or more program modules, and the one or more program modules are stored in the memory 21, and It is executed by one or more processors (the processor 22 in this embodiment) to complete the application.
  • FIG. 3 is a schematic diagram of a program module of the first embodiment of the face recognition intelligent comparison system 20.
  • the face recognition intelligent comparison system 20 can be divided into ID card information collection.
  • the program module referred to in the present application refers to a series of computer program instruction segments capable of performing a specific function.
  • the specific functions of the program modules 201-205 are described in detail in the third embodiment, and details are not described herein again.
  • the ID card collector 24 is configured to be connected to the ID card information collection module 201, and collect ID information stored by the user, such as pre-stored information in the chip of the second generation ID card.
  • the camera 25 is configured to be activated and deactivated by the face recognition module 203 to collect a face image of the operation terminal device.
  • a flash 26 is also included that is configured to be activated and deactivated by the shooting adjustment subroutine and adjusted in brightness.
  • the embodiment provides a computer readable storage medium on which the face recognition intelligent comparison system 20 is stored, when the face recognition intelligent comparison system 20 is executed by one or more processors.

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Abstract

La présente invention concerne un système et un procédé de comparaison intelligente de reconnaissance faciale, un dispositif électronique et un support d'informations lisible par ordinateur. Le procédé comprend les étapes suivantes consistant : étape 01, à acquérir des informations d'une carte ID d'un utilisateur à identifier ; étape 02, à déterminer si la carte ID est valide, et si tel est le cas, à exécuter l'étape 03, et si tel n'est pas le cas, à indiquer que la carte ID n'est pas valide ; étape 03, à réaliser une comparaison de reconnaissance faciale afin d'obtenir un premier seuil de comparaison ; étape 04, à déterminer, en fonction des informations sur la carte ID, un groupe auquel l'utilisateur appartient, et à obtenir un seuil de référence de comparaison de reconnaissance faciale du groupe ; et étape 05, à déterminer si le premier seuil de comparaison est supérieur ou égal au seuil de référence de comparaison de reconnaissance faciale, et si tel est le cas, à indiquer que la reconnaissance a réussi, et si tel n'est pas le cas, à indiquer que la reconnaissance a échoué. Le procédé et le système résolvent le problème de l'état de la technique selon lequel l'utilisation d'un seuil de comparaison uniforme diminue le taux de réussite de correspondance, améliorent les taux de reconnaissance de différents groupes, et améliorent l'efficacité de traitement de service et la satisfaction du client.
PCT/CN2018/083071 2017-10-31 2018-04-13 Procédé de comparaison intelligente de reconnaissance faciale, dispositif électronique et support d'informations lisible par ordinateur WO2019085403A1 (fr)

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Families Citing this family (34)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060158307A1 (en) * 2005-01-13 2006-07-20 Samsung Electronics Co., Ltd. System and method for face recognition
CN105243357A (zh) * 2015-09-15 2016-01-13 深圳市环阳通信息技术有限公司 一种基于身份证件的人脸识别方法及人脸识别装置
CN105447367A (zh) * 2015-11-13 2016-03-30 中国建设银行股份有限公司 自助发卡设备及其身份认证***和身份认证方法
CN105930709A (zh) * 2016-04-21 2016-09-07 深圳泰首智能技术有限公司 人脸识别技术应用于人证一致性检验的方法及装置
CN107818308A (zh) * 2017-10-31 2018-03-20 平安科技（深圳）有限公司 一种人脸识别智能比对方法、电子装置及计算机可读存储介质

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060158307A1 (en) * 2005-01-13 2006-07-20 Samsung Electronics Co., Ltd. System and method for face recognition
CN105243357A (zh) * 2015-09-15 2016-01-13 深圳市环阳通信息技术有限公司 一种基于身份证件的人脸识别方法及人脸识别装置
CN105447367A (zh) * 2015-11-13 2016-03-30 中国建设银行股份有限公司 自助发卡设备及其身份认证***和身份认证方法
CN105930709A (zh) * 2016-04-21 2016-09-07 深圳泰首智能技术有限公司 人脸识别技术应用于人证一致性检验的方法及装置
CN107818308A (zh) * 2017-10-31 2018-03-20 平安科技（深圳）有限公司 一种人脸识别智能比对方法、电子装置及计算机可读存储介质

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110490039A (zh) * 2019-05-30 2019-11-22 福建知鱼科技有限公司 一种智能人像比对***
US11380115B2 (en) * 2019-06-04 2022-07-05 Idemia Identity & Security USA LLC Digital identifier for a document
CN110647805A (zh) * 2019-08-09 2020-01-03 平安科技（深圳）有限公司 一种网纹图像识别方法、装置及终端设备
CN110647805B (zh) * 2019-08-09 2023-10-31 平安科技（深圳）有限公司 一种网纹图像识别方法、装置及终端设备
CN111079568A (zh) * 2019-11-28 2020-04-28 乌鲁木齐明华智能电子科技有限公司 一种采用5g网络通讯的动态人脸识别终端
CN111144326A (zh) * 2019-12-28 2020-05-12 神思电子技术股份有限公司 一种用于人机交互的人脸防重识别方法
CN111144326B (zh) * 2019-12-28 2023-10-27 神思电子技术股份有限公司 一种用于人机交互的人脸防重识别方法
CN111213920A (zh) * 2020-01-08 2020-06-02 宁波高新区阶梯科技有限公司 一种电子烟及其启动控制方法、装置以及可读存储介质
CN111507165A (zh) * 2020-01-21 2020-08-07 北京明略软件***有限公司 人脸识别方法、装置、电子设备及计算机可读存储介质
CN111597532A (zh) * 2020-04-10 2020-08-28 云知声智能科技股份有限公司 基于人脸识别实现儿童机器人童锁***的方法和***
CN111597532B (zh) * 2020-04-10 2023-11-17 云知声智能科技股份有限公司 基于人脸识别实现儿童机器人童锁***的方法和***
CN111523103A (zh) * 2020-05-09 2020-08-11 支付宝(杭州)信息技术有限公司 用户身份识别方法、装置及电子设备
CN111814551A (zh) * 2020-06-06 2020-10-23 中信百信银行股份有限公司 基于小程序的人脸识别方法、装置、设备及存储介质
CN112287830A (zh) * 2020-10-29 2021-01-29 泰康保险集团股份有限公司 一种图像的检测方法及装置
CN113379713A (zh) * 2021-06-23 2021-09-10 京东数科海益信息科技有限公司 证件图像的检测方法及其装置
CN113379713B (zh) * 2021-06-23 2024-02-09 京东科技信息技术有限公司 证件图像的检测方法及其装置
CN113486319A (zh) * 2021-08-02 2021-10-08 安徽文香科技有限公司 一种在线教育平台的用户认证方法及装置

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