WO2021047069A1 - Procédé de reconnaissance faciale et dispositif terminal électronique - Google Patents

Procédé de reconnaissance faciale et dispositif terminal électronique Download PDF

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Publication number
WO2021047069A1
WO2021047069A1 PCT/CN2019/122508 CN2019122508W WO2021047069A1 WO 2021047069 A1 WO2021047069 A1 WO 2021047069A1 CN 2019122508 W CN2019122508 W CN 2019122508W WO 2021047069 A1 WO2021047069 A1 WO 2021047069A1
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WIPO (PCT)
Prior art keywords
information
face recognition
recognized
recognition method
display information
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PCT/CN2019/122508
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English (en)
Chinese (zh)
Inventor
张台峰
周凡贻
尚国强
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深圳传音控股股份有限公司
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Publication of WO2021047069A1 publication Critical patent/WO2021047069A1/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
    • G06V40/161—Detection; Localisation; Normalisation
    • 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
    • G06V40/103—Static body considered as a whole, e.g. static pedestrian or occupant 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
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification
    • G—PHYSICS
    • 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

  • face recognition technology has been widely used in enterprise and residential security and management due to its advantages, such as face recognition access control and attendance systems, face recognition anti-theft doors, etc., as well as public security, judicial and criminal investigations (for example, the use of human Facial recognition systems and networks hunt down fugitives across the country); self-service (for example, bank ATMs. If facial recognition is applied at the same time, it will largely avoid the occurrence of cash stolen by others, and information security ( For example, computer login, e-government and e-commerce) and enhancing the fun of taking pictures with cameras.
  • Face recognition technology includes at least the following advantages: non-contact, that is, the user does not need to directly contact the device; non-mandatory, that is, the recognized face image information can be actively obtained; concurrency, that is, multiple people can be used in actual application scenarios. Sorting, judgment and recognition of faces.
  • the age of the faces appearing in the pictures will be estimated, and the age will be displayed on the face detection frame information.
  • the recognition of the user's age in the prior art is limited to the correction between the face template database and its own age estimation algorithm, lacks the ability to learn and update the adaptive feature database, and is inaccurate for the user's age recognition;
  • the positioning accuracy of the face template database positioning algorithm used in the prior art is affected by the photographing environment in which the camera is located, that is, the brightness of the photographing environment, the posture of the collected face image, and the difference of the face image. Changes in external photographing conditions such as facial expression changes will reduce the positioning accuracy of the face template database positioning algorithm, which in turn will reduce the accuracy of user age recognition.
  • the purpose of this application is to provide a face recognition method and electronic terminal equipment to solve the lack of adaptive feature database learning and updating capabilities of the face template database in the prior art, and the positioning of the face template database positioning algorithm.
  • the accuracy is affected by the photographing environment in which the camera is located, which leads to a problem that the accuracy of the user's age identification is reduced.
  • the judging whether the portrait pose in the target portrait image to be recognized is a standard pose; if so, matching the target portrait image to be recognized with the face image samples in the feature database to obtain The identification information,
  • the identification information is a matching face image sample.
  • the method further includes: using an age estimation method to estimate the portrait image of the target to be recognized User age.
  • the display information is user information, including one or any combination of age information, hobby information, facial features information, skin color information, and skin state information of the user.
  • the step of correcting the display information includes: manually modifying the display information when the error of the age information is not within a preset range.
  • this application also provides a face recognition method.
  • it includes: acquiring a target portrait image to be recognized; identifying and displaying the display information of the target portrait; when the terminal receives the display information Error signal; capture new input information, import the new input information into the feature database to update.
  • the manner in which the terminal captures the new input information includes one or any combination of voice input, file import, and text input.
  • the specific steps of the terminal capturing and judging the user's emotional information to obtain the display information error signal include:
  • the terminal After the terminal displays the display information of the target portrait, it calls the terminal shooting component, voice input component or infrared sensor to obtain the user's expression and sound after seeing the display information, and judge the user's emotional information.
  • this application also provides a face recognition method. As one of the implementation manners, it includes:
  • the display information of the object to be identified is generated according to the identification information.
  • the method further includes a step of classifying the object to be identified according to the pixel set, and the object to be identified is a portrait image of the object to be identified.
  • the present application also provides an electronic terminal device, including: a processor, a memory, and a computer program stored on the memory and running on the processor, and the processor executes the program When realizing the face recognition method as described above.
  • this application also provides a computer-readable storage medium.
  • the computer-readable storage medium stores a face recognition program
  • the face recognition program is processed by a processor.
  • the steps of the face recognition method as described above are realized.
  • the face recognition method provided by the present application includes: a terminal receives a display information error signal, captures new input information, and imports the new input information into the feature database to update. It can be seen that the face recognition method of the present application has learning capabilities, which can be an adaptive feature database that can be continuously updated. When the face recognition method described above is used to recognize a face, it can improve face recognition. Accuracy, improve user experience.
  • the present application also provides a face recognition method, including: acquiring a target portrait image to be recognized; extracting identification information of the target portrait image according to a judgment rule, and generating display information of the target portrait based on the identification information. That is to say, the posture of the target portrait image to be recognized is specifically judged. When the judgment result is a free posture, the posture of the target portrait image to be recognized can be corrected, and the corrected target portrait image to be recognized and the feature database are used.
  • the matching of face image samples makes face recognition not affected by the camera's photographing environment, and improves the accuracy of face recognition. At the same time, it can also be based on the user's voluntary information sharing, that is, when the user believes that the currently identified age information does not meet the actual age, upload the information. After uploading the information, at least one sample information in the characteristic database is updated, and then, Then, the target portrait image to be recognized is matched with the facial image samples in the updated feature database to improve the accuracy of age recognition.
  • FIG. 1 is a schematic flowchart of a face recognition method provided by an embodiment of this application.
  • the recognition of the user’s age in the prior art is limited to the correction between the face template database and its own age estimation algorithm, and lacks the ability to learn and update the self-adaptive feature database.
  • Recognition is not accurate; on the other hand, the positioning accuracy of the face template database positioning algorithm used in the prior art is affected by the photographing environment in which the camera is located, that is, the brightness of the photographing environment and the difference between the collected face images Changes in external photographing conditions, such as postures and facial expression changes in facial images, will reduce the positioning accuracy of the facial template database positioning algorithm, which in turn will reduce the accuracy of user age recognition.
  • the age estimation schemes when taking pictures are based on image vision algorithms, which extract facial appearance features, establish a regression or classification relationship model between features and age values, and then use this relationship model to predict the age of the newly photographed face
  • image vision algorithms which extract facial appearance features, establish a regression or classification relationship model between features and age values, and then use this relationship model to predict the age of the newly photographed face
  • the image is greatly affected by factors such as light, hairstyle, occlusion, image resolution, makeup, etc.
  • the age estimation algorithm based purely on image vision has a large deviation in age display, and the same person takes pictures in different poses, or Taking pictures under different light will show different ages, which will make users feel that the age algorithm of the camera is not reliable, and affect the user experience.
  • the present application provides a face recognition method through an embodiment, which includes the following processes: obtain a target portrait image to be recognized; extract the recognition information of the target portrait image according to the judgment rule, and generate a face recognition method based on the recognition information. Describes the display information of the target portrait. Specifically, the posture of the target image to be recognized is judged. When the result of the judgment is a free posture, the posture of the target image to be recognized can be corrected, and the corrected image of the target person to be recognized and the feature database can be used. Matching of face image samples makes face recognition not affected by the camera's photographing environment, and improves the accuracy of face recognition.
  • the target portrait image to be recognized is matched with the facial image samples in the updated feature database to improve the accuracy of age recognition.
  • the present application also provides a face recognition method through another embodiment, including: acquiring a target portrait image to be recognized; identifying and displaying display information of the target portrait; when the terminal receives a display information error signal; Capture new input information and import the new input information into the feature database to update.
  • the face recognition method of the present application has learning capabilities, which can be an adaptive feature database that can be continuously updated.
  • a face recognition method of this embodiment includes the following processes: acquiring a target portrait image to be recognized; extracting recognition information of the target portrait image according to a judgment rule, and generating display information of the target portrait according to the recognition information.
  • the feature database is searched to obtain the user information corresponding to the matched face image sample.
  • the identification information is a matching face image sample.
  • the method further includes: using an age estimation method to estimate the age of the user based on the to-be-recognized target portrait image.
  • the display information is user information, including one or any combination of the user's age information, hobby information, facial features information, skin color information, and skin state information.
  • the display information is corrected and the characteristic database is updated.
  • the step of correcting the display information includes: manually modifying the display information when the error of the age information is not within a preset range.
  • the posture of the target portrait image to be recognized when the result of the determination is a free posture, the posture of the target portrait image to be recognized can be corrected, and the corrected target portrait image to be recognized and the feature database can be used
  • Matching of face image samples makes face recognition not affected by the camera's photographing environment, and improves the accuracy of face recognition.
  • it can also be based on the user's voluntary information sharing, that is, when the user believes that the currently identified age information does not meet the actual age, upload the information. After uploading the information, at least one sample information in the characteristic database is updated, and then, Then, the target portrait image to be recognized is matched with the facial image samples in the updated feature database to improve the accuracy of age recognition.
  • step S1 is to obtain a portrait image of a target to be recognized.
  • step S2 It is judged whether the portrait posture in the target portrait image to be recognized is a standard posture; if yes, go to step S3, if not, go to step S4.
  • Step S3 Match the target portrait image to be recognized with the face image sample in the feature database to obtain a matching face image sample, and then proceed to step S5.
  • step S4 Calling the posture correction model library, correcting the target portrait image to be recognized, and obtaining a target portrait image similar to the standard posture, and proceeding to step S6.
  • Step S5 Search a feature database to obtain user information corresponding to the matched face image sample.
  • Step S6 Match the face image of the target to be recognized in the approximate standard posture with the face image sample in the feature database to obtain a matching face image sample, and then enter the step S5.
  • Step S7 Display user information.
  • the user information includes age information.
  • the feature database stores several face image samples and user information corresponding to each of the face image samples.
  • the method further includes: step S5.1, adopting an age estimation method according to the target portrait image to be recognized Estimate the age of the user and proceed to the step S7.
  • one embodiment of the present application also provides a face recognition method to obtain a target portrait image to be recognized; recognize and display the display information of the target portrait; when the terminal receives a display information error signal; capture new input information , Import the new input information into the feature database to update.
  • the new input information is imported into the feature database for updating, it further includes the step of displaying new display information, the display information including user age information, hobby information, facial features information, skin color information, and skin status information.
  • the display information including user age information, hobby information, facial features information, skin color information, and skin status information.
  • the manner in which the terminal captures the new input information includes one or any combination of voice input, file import, and text input.
  • the step of the terminal receiving the display information error signal specifically includes: the user actively touches the display information to remind the display information error or the terminal captures and judges the user's emotional information to obtain the display information error signal.
  • the specific steps of the terminal capturing and judging the user's emotional information to obtain the display information error signal include:
  • the terminal After the terminal displays the display information of the target portrait, it calls the terminal shooting component, voice input component or infrared sensor to obtain the user's expression and sound after seeing the display information, and judge the user's emotional information.
  • an embodiment of the present application also provides a face recognition method
  • the display information of the object to be identified is generated according to the identification information.
  • the method further includes a step of classifying the object to be identified according to the pixel set, and the object to be identified is a portrait image of the target to be identified.
  • the feature database provided by an embodiment of the present application has learning capabilities. It can be an adaptive feature database that can be continuously updated.
  • face recognition method is used to recognize faces, it can improve Face recognition accuracy improves user experience.
  • an embodiment of the present application also discloses an electronic terminal device, including: a processor 100, a memory 101, and a computer program stored in the memory and capable of running on the processor.
  • a processor 100 executes the program, the face recognition method described above is implemented.
  • the electronic terminal device further includes: a power supply module 102, an interactive component 103, a communication module 104, a sensor module 105, and an interface 106.
  • the processor 100 generally performs overall operations of an electronic terminal device, such as operations associated with display, telephone call, data communication, camera operations, and recording operations.
  • the processor 100 may include one or more modules to facilitate interaction between the processor 100 and other modules.
  • the processor 100 may include a multimedia module to facilitate the interaction between the interactive component 103 and the processor 100.
  • the memory 101 is configured to store various types of data to support operations in the electronic terminal device. Examples of these data include instructions for any application or method operated on the electronic terminal device, contact data, phone book data, messages, pictures, videos, etc.
  • the power supply module 102 provides power for various modules of the electronic terminal device.
  • the power supply module 102 may include a power management system, one or more power supplies, and other modules associated with generating, managing, and distributing power for electronic terminal devices.
  • the interaction component 103 includes a screen that provides an output interface between the electronic terminal device and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user.
  • the touch panel includes one or more touch sensors to sense touch, sliding, and gestures on the touch panel. The touch sensor can not only sense the boundary of the touch or slide action, but also detect the duration and pressure related to the touch or slide operation.
  • the sensor module 105 includes one or more sensors for providing various aspects of state evaluation for the electronic terminal device.
  • the sensor module 105 can detect the on/off status of the electronic terminal device and the relative positioning of the modules, such as the display and keypad of the electronic terminal device.
  • the sensor module 105 can also detect the electronic terminal device or a component of the electronic terminal device. The position changes, the presence or absence of contact between the user and the electronic terminal device, the orientation or acceleration/deceleration of the electronic terminal device and its temperature change.
  • the sensor module 105 may include a proximity sensor configured to detect the presence of nearby objects when there is no physical contact.
  • the sensor module 105 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • an embodiment of the present application further provides a computer-readable storage medium, including: a face recognition program is stored on the computer-readable storage medium, and when the face recognition program is executed by a processor Implement the steps of the face recognition method as described above.
  • computer-readable storage media include: electrical connections with one or more wires, portable computer hard disks, hard disks, random access memory (RAM), read-only memory (ROM), Erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.
  • the computer-readable signal medium may include a data signal propagated in baseband or as a part of a carrier wave, and computer-readable program code is carried therein. This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • the computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium.
  • the computer-readable medium may send, propagate, or transmit the program for use by or in combination with the instruction execution system, apparatus, or device .
  • the remote computer can be connected to the user's computer through any kind of network-including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect via the Internet) ).
  • LAN local area network
  • WAN wide area network
  • each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be used in a dedicated hardware-based system that performs the specified functions or actions. It can be realized, or it can be realized by a combination of dedicated hardware and computer instructions.
  • each module may exist alone, or two or more modules may be integrated to form an independent part.
  • the target portrait image to be recognized is matched with the facial image samples in the updated feature database, so as to improve the accuracy of age recognition.
  • the present application also provides a face recognition method through another embodiment, including: acquiring a target portrait image to be recognized; identifying and displaying display information of the target portrait; when the terminal receives a display information error signal; Capture new input information and import the new input information into the feature database to update.
  • the feature database of the present application has learning capabilities, and it can be an adaptive feature database that can be continuously updated.

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  • Engineering & Computer Science (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un procédé de reconnaissance faciale et un dispositif terminal électronique pouvant améliorer la précision de reconnaissance. Le procédé comprend les étapes consistant à : obtenir une image portrait cible à reconnaître (S1) ; déterminer si une posture de portrait dans l'image de portrait cible à reconnaître est une posture standard (S2) ; et extraire des informations de reconnaissance de l'image portrait cible, et générer des informations d'affichage du portrait cible selon les informations de reconnaissance.
PCT/CN2019/122508 2019-09-11 2019-12-02 Procédé de reconnaissance faciale et dispositif terminal électronique WO2021047069A1 (fr)

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CN201910858770.2 2019-09-11
CN201910858770.2A CN110717399A (zh) 2019-09-11 2019-09-11 人脸识别方法和电子终端设备

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113420666A (zh) * 2021-06-23 2021-09-21 上海应用技术大学 一种基于人脸识别技术的记忆辅助方法及装置
CN114049658A (zh) * 2021-09-18 2022-02-15 特斯联科技集团有限公司 基于人脸识别的流动人口管理方法、装置、计算机设备和存储介质
CN114140856A (zh) * 2021-12-07 2022-03-04 广联达科技股份有限公司 人脸识别方法、装置、系统、计算机设备和可读存储介质
CN114283464A (zh) * 2021-11-26 2022-04-05 珠海格力电器股份有限公司 一种提高人脸识别的方法及系统、智能终端
CN114821513A (zh) * 2022-06-29 2022-07-29 威海凯思信息科技有限公司 一种基于多层网络的图像处理方法及装置、电子设备
CN115471893A (zh) * 2022-09-16 2022-12-13 北京百度网讯科技有限公司 训练人脸识别模型、人脸识别的方法和装置

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111428576B (zh) * 2020-03-02 2024-04-26 广州微盾科技股份有限公司 特征信息学习方法、电子设备及存储介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105989331A (zh) * 2015-02-11 2016-10-05 佳能株式会社 脸部特征提取装置、脸部特征提取方法、图像处理设备和图像处理方法
US20170140210A1 (en) * 2015-11-16 2017-05-18 Canon Kabushiki Kaisha Image processing apparatus and image processing method
CN107622261A (zh) * 2017-11-03 2018-01-23 北方工业大学 基于深度学习的人脸年龄估计方法及装置
CN109977781A (zh) * 2019-02-26 2019-07-05 上海上湖信息技术有限公司 人脸检测方法及装置、可读存储介质

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102917120A (zh) * 2012-09-20 2013-02-06 北京百纳威尔科技有限公司 移动终端显示信息的刷新方法及移动终端
CN107133576A (zh) * 2017-04-17 2017-09-05 北京小米移动软件有限公司 用户年龄识别方法及装置
CN108229420B (zh) * 2018-01-22 2020-10-16 维沃移动通信有限公司 一种人脸识别方法、移动终端
CN109583972A (zh) * 2018-12-19 2019-04-05 中新智擎科技有限公司 一种广告展示方法、装置、广告机器人及存储介质

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105989331A (zh) * 2015-02-11 2016-10-05 佳能株式会社 脸部特征提取装置、脸部特征提取方法、图像处理设备和图像处理方法
US20170140210A1 (en) * 2015-11-16 2017-05-18 Canon Kabushiki Kaisha Image processing apparatus and image processing method
CN107622261A (zh) * 2017-11-03 2018-01-23 北方工业大学 基于深度学习的人脸年龄估计方法及装置
CN109977781A (zh) * 2019-02-26 2019-07-05 上海上湖信息技术有限公司 人脸检测方法及装置、可读存储介质

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113420666A (zh) * 2021-06-23 2021-09-21 上海应用技术大学 一种基于人脸识别技术的记忆辅助方法及装置
CN114049658A (zh) * 2021-09-18 2022-02-15 特斯联科技集团有限公司 基于人脸识别的流动人口管理方法、装置、计算机设备和存储介质
CN114283464A (zh) * 2021-11-26 2022-04-05 珠海格力电器股份有限公司 一种提高人脸识别的方法及系统、智能终端
CN114140856A (zh) * 2021-12-07 2022-03-04 广联达科技股份有限公司 人脸识别方法、装置、系统、计算机设备和可读存储介质
CN114821513A (zh) * 2022-06-29 2022-07-29 威海凯思信息科技有限公司 一种基于多层网络的图像处理方法及装置、电子设备
CN115471893A (zh) * 2022-09-16 2022-12-13 北京百度网讯科技有限公司 训练人脸识别模型、人脸识别的方法和装置
CN115471893B (zh) * 2022-09-16 2023-11-21 北京百度网讯科技有限公司 训练人脸识别模型、人脸识别的方法和装置

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