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WO2016015621A1 - Procédé et système de reconnaissance de nom d'image de visage humain - Google Patents

Procédé et système de reconnaissance de nom d'image de visage humain Download PDF

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Publication number
WO2016015621A1
WO2016015621A1 PCT/CN2015/085285 CN2015085285W WO2016015621A1 WO 2016015621 A1 WO2016015621 A1 WO 2016015621A1 CN 2015085285 W CN2015085285 W CN 2015085285W WO 2016015621 A1 WO2016015621 A1 WO 2016015621A1
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WIPO (PCT)
Prior art keywords
face
name
person
picture
target
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Ceased
Application number
PCT/CN2015/085285
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English (en)
Chinese (zh)
Inventor
胡金辉
赵瑾
朱茂清
陶哲
郭毓琦
韩玉刚
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Qihoo Technology Co Ltd
Qizhi Software Beijing Co Ltd
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Beijing Qihoo Technology Co Ltd
Qizhi Software Beijing Co Ltd
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Publication date
Application filed by Beijing Qihoo Technology Co Ltd, Qizhi Software Beijing Co Ltd filed Critical Beijing Qihoo Technology Co Ltd
Publication of WO2016015621A1 publication Critical patent/WO2016015621A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V40/179—Human faces, e.g. facial parts, sketches or expressions metadata assisted face recognition

Definitions

  • the present invention relates to the field of computer technologies, and in particular, to a method and system for recognizing a human face picture.
  • the conventional scheme for recognizing a person's name corresponding to a face is generally as follows: directly comparing a face image of an unknown person's name with a face image of a known person's name, and if all or most of the two are the same, it can be judged in the two pictures.
  • the human face is the face of the same person, that is, the name of the face image of the unknown person name can be determined according to the name of the face image of the known person name.
  • the main disadvantage of this scheme is that since it is a comparison of two pictures, there are very high requirements on the expression, angle, size, etc. of the faces in the two pictures, which easily makes it difficult to match the faces of the same figure in the two pictures. However, it is impossible to accurately identify, and eventually the name of the person whose unknown face image cannot be judged, the recognition failure rate is very high.
  • the present invention has been made in order to provide a face picture person name recognition method and system that overcomes the above problems or at least partially solves the above problems.
  • a method for recognizing a face picture name including: setting a corresponding person name for a collected face picture; and performing a picture including the target face and the collected face picture Comparing, identifying one or more face pictures similar to the picture containing the target face; determining the name of the target face according to the name of the person corresponding to the similar one or more face pictures.
  • a face picture person name recognition system comprising: a person name setting module, configured to set a corresponding person name for the collected face picture; a similar face picture recognition module, Comparing a picture containing the target face with the collected face picture, identifying one or more face pictures similar to the picture containing the target face; a name determination module for using the similar The name of the person corresponding to the one or more face images respectively determines the name of the person of the target face.
  • a computer program comprising a computer readable generation
  • a code when the computer readable code is run on a computing device, causes the computing device to perform the method described in the preceding paragraph.
  • a computer readable medium storing the computer program described in the preceding paragraph is provided.
  • the face picture person name recognition method and system of the present invention have at least the following advantages:
  • the present invention does not directly perform the comparison between the individual face images, but firstly recognizes a similar face image from the collected face images based on the face recognition technology, and the image containing the target face is usually It is a picture of the same face, so based on the name of a similar face picture, the name of the target face can be determined; based on the face recognition technology to identify similar face pictures, the target face is less demanding in terms of expression and angle. Therefore, it is easier to identify different face images of the same person corresponding to the target face, that is, it is easier to determine the name of the target face.
  • FIG. 1 shows a flow chart of a face picture person name recognition method according to an embodiment of the present invention
  • FIG. 2 is a flow chart showing the working principle of a face picture person name recognition method according to an embodiment of the present invention
  • FIG. 3 illustrates a partial flowchart of a face picture person name recognition method according to an embodiment of the present invention
  • FIG. 4 shows a block diagram of a face picture person name recognition system in accordance with one embodiment of the present invention
  • FIG. 5 is a block diagram showing a face picture person name recognition method according to an embodiment of the present invention.
  • FIG. 6 is a partial block diagram showing a face picture person name recognition method according to an embodiment of the present invention.
  • Figure 7 shows a block diagram of a computing device for performing a method in accordance with the present invention
  • Figure 8 shows a storage unit for holding or carrying program code implementing the method according to the invention.
  • an embodiment of the present invention provides a method for recognizing a human face picture name, including:
  • Step 110 Set a corresponding person name for the collected face image.
  • the manner of setting the name of the person is not limited. Specifically, the corresponding name is recorded when each face image is collected.
  • Step 120 Comparing the picture containing the target face with the collected face picture, and identifying one or more face pictures similar to the picture containing the target face. According to the existing various face recognition technologies, similar face pictures can be better recognized.
  • Step 130 Determine a person name of the target face according to the name of the person corresponding to the similar one or more face images.
  • the manner of determining the name of the target face is not limited. For example, in a case where the names of the plurality of similar face pictures are different, the name of the person who has a higher number of occurrences may be selected as the target face. Name of person.
  • the comparison between the single face images is not directly performed, but the similar face images are firstly recognized from the collected face images based on the face recognition technology.
  • the picture with the target face is usually the same face image, so based on the name of the similar face picture, the person name of the target face can be determined; the similar face image is recognized based on the face recognition technology, and the target face is in the expression
  • the requirements for angles and angles are lower, so it is easier to identify different face images of the same person corresponding to the target face, that is, it is easier to determine the name of the target face.
  • Another embodiment of the present invention further provides a method for recognizing a human face picture name, where the step 110 specifically includes:
  • the person's name is extracted from the related text of the collected face image as the name of the person corresponding to the collected face image.
  • the form of the related text is not limited. For example, if the face picture appears in the news, the related text may be the title or body of the news.
  • Another embodiment of the present invention further provides a method for recognizing a human face picture name, wherein the step 130 specifically includes:
  • the highest degree of similarity indicates that the two face pictures are most likely to be the face pictures of the same person, and therefore should have the same person name.
  • Another embodiment of the present invention further provides a method for recognizing a human face picture name, wherein the step 130 specifically includes:
  • the name of the corresponding face image of the maximum similarity is taken as the name of the target face.
  • the specific size of the first threshold is not limited, for example, it may be 90%. If the maximum similarity is greater than 90%, it is considered that the two faces are very similar and should be the face of the same person, and the target face belongs to the face of the character corresponding to the name, and the name can be output for the user at this time.
  • Another embodiment of the present invention further provides a method for recognizing a human face picture name, wherein the step 130 specifically includes:
  • the plurality of similar face images may be face images of different characters, wherein the face image with the same person name should be the face image of the same person, and the similarity is the largest after the addition. It means that the corresponding person is the most similar to the character corresponding to the current face, and should be the same person, so it should have the same person name.
  • Another embodiment of the present invention further provides a method for recognizing a human face picture name, wherein the step 130 specifically includes:
  • the name of the person whose maximum similarity is added is taken as the name of the person of the target face.
  • the specific size of the second threshold is not limited. For example it can be 200%. If the maximum similarity after the addition is greater than 200%, it is considered that the face image corresponding to the person name is very similar to the target face, and should be the face of the same person, and the target face belongs to the face of the person corresponding to the name. At this time, the name of the person can be output for the user.
  • Another embodiment of the present invention further provides a face picture person name recognition method, further comprising: acquiring, after the added maximum similarity is lower than a predetermined second threshold, the person corresponding to the maximum similarity after the addition Face images are provided to the user.
  • the maximum similarity after the addition when the maximum similarity after the addition is lower than a certain level, it means that the image containing the target face in any similar face image does not correspond to the same person, so the accuracy cannot be accurate at this time.
  • the person name of the target face is determined, and the most similar face image can be provided to the user for the user to judge whether it is the face of the same person.
  • the operation ends. If the face is included, the subsequent operation is continued, and the face is the target face that needs to identify the corresponding person name; Face recognition, identifying a plurality of face images similar to the image containing the target face; taking similarities between similar multiple face images and the image containing the target face, such as where the maximum similarity is greater than the first threshold, then The person whose face image corresponds to the maximum similarity is the name of the person whose face is the target face; if the maximum similarity is lower than the first threshold, the similarities corresponding to the face images of the same person name are added, such as the maximum of the added If the similarity is greater than the second threshold, the person name corresponding to the maximum similarity after the addition is taken as the name of the target face, otherwise the most similar face may be directly output.
  • another embodiment of the present invention further provides a method for recognizing a human face picture name, where the step 120 specifically includes:
  • Step 121 Extract features in the collected face images and store them in a preset face database.
  • the face in the picture is automatically detected for the collected face picture, and then the feature of the face is extracted and quantized into a high-dimensional vector.
  • Using high-dimensional vectors to represent faces can reduce the amount of data and facilitate subsequent similar face comparisons.
  • This will create a collection of people A database of face image features.
  • step 122 the feature of the picture containing the target face is extracted and compared with the feature of the collected face picture taken from the face database.
  • the face is automatically detected first, the feature of the face is extracted, and quantized into a high-dimensional vector; the vector and the library containing the picture of the target face
  • the feature high-dimensional vectors of the inner face images are compared, and the Euclidean distance is calculated, and the first N vectors closest to each other, that is, the first N similar faces are taken.
  • the face database is too large, it takes a long time to compare one by one, you can cluster the face in the library in advance, and then only compare with the face of the cluster, which can greatly shorten the comparison time; the specific comparison is high.
  • the dimensional feature vectors are compared, the Euclidean distance between the vectors is calculated, and the first N vectors closest to each other are taken.
  • the faces represented by these vectors are the faces most similar to the input face.
  • another embodiment of the present invention further provides a face picture name recognition system, including:
  • the person name setting module 410 is configured to set a corresponding person name for the collected face picture.
  • the manner of setting the name of the person is not limited. Specifically, the corresponding name is recorded when each face image is collected.
  • the similar face image recognition module 420 is configured to compare the picture containing the target face with the collected face picture, and identify one or more face pictures similar to the picture containing the target face. According to the existing various face recognition technologies, similar face pictures can be better recognized.
  • the person name determining module 430 is configured to determine the person name of the target face according to the name of the person corresponding to the similar one or more face pictures.
  • the manner of determining the name of the target face is not limited. For example, in a case where the names of the plurality of similar face pictures are different, the name of the person who has a higher number of occurrences may be selected as the target face. Name of person.
  • the comparison between the single face images is not directly performed, but the similar face images are firstly recognized from the collected face images based on the face recognition technology.
  • the picture with the target face is usually the same face image, so based on the name of the similar face picture, the person name of the target face can be determined; the similar face image is recognized based on the face recognition technology, and the target face is in the expression
  • the requirements for angles and angles are lower, so it is easier to identify different face images of the same person corresponding to the target face, that is, it is easier to determine the name of the target face.
  • Another embodiment of the present invention further provides a face picture person name recognition system, wherein the person name setting module 410 extracts a person name from the related text of the collected face picture as the name of the person corresponding to the collected face picture.
  • the form of the related text is not limited. For example, if the face picture appears in the news, the related text may be the title or body of the news.
  • Another embodiment of the present invention further provides a face picture person name recognition system, wherein the person name determination 430 acquires the similarity between the similar one or more face pictures and the picture including the target face, and correspondingly the maximum similarity thereof.
  • the name of the face image is the name of the person who is the target face.
  • the highest degree of similarity indicates that the two face pictures are most likely to be the face pictures of the same person, and therefore should have the same person name.
  • Another embodiment of the present invention further provides a face picture person name recognition system, wherein the person name determination 430 uses the person name of the corresponding face image of the maximum similarity as the target person when the maximum similarity is higher than the predetermined first threshold.
  • the name of the face In this embodiment, the specific size of the first threshold is not limited, for example, it may be 90%. If the maximum similarity is greater than 90%, it is considered that the two faces are very similar and should be the face of the same person, and the target face belongs to the face of the character corresponding to the name, and the name can be output for the user at this time.
  • Another embodiment of the present invention further provides a face picture person name recognition system, wherein the person name determining module 430 acquires the similarity between the similar one or more face pictures and the picture containing the target face, and the person with the same person name The similarities corresponding to the face images are added, and the maximum similarities after the addition are corresponding. The name of the person who is the target face.
  • the plurality of similar face images may be face images of different characters, wherein the face image with the same person name should be the face image of the same person, and the similarity is the largest after the addition. It means that the corresponding person is the most similar to the character corresponding to the current face, and should be the same person, so it should have the same person name.
  • Another embodiment of the present invention further provides a face picture person name recognition system, wherein the person name determining module 430 adds the maximum similarity when the added maximum similarity is higher than a predetermined second threshold.
  • the name of the person who is the target face In this embodiment, the specific size of the second threshold is not limited, for example, it may be 200%. If the maximum similarity after the addition is greater than 200%, it is considered that the face image corresponding to the person name is very similar to the target face, and should be the face of the same person, and the target face belongs to the face of the person corresponding to the name. At this time, the name of the person can be output for the user.
  • another embodiment of the present invention further provides a face picture name recognition system, which further includes:
  • the similar face image providing module 440 is configured to obtain a face image corresponding to the maximum similarity after the addition, when the maximum similarity after the addition is lower than the predetermined second threshold, to be provided to the user.
  • the maximum similarity after the addition when the maximum similarity after the addition is lower than a certain level, it means that the image containing the target face in any similar face image does not correspond to the same person, so the accuracy cannot be accurate at this time.
  • the person name of the target face is determined, and the most similar face image can be provided to the user for the user to judge whether it is the face of the same person.
  • the operation ends. If the face is included, the subsequent operation is continued, and the face is the target face that needs to identify the corresponding person name; Face recognition, identifying a plurality of face images similar to the image containing the target face; taking similarities between similar multiple face images and the image containing the target face, such as where the maximum similarity is greater than the first threshold, then The person whose face image corresponds to the maximum similarity is the name of the person whose target face is; If the maximum similarity is lower than the first threshold, the similarities corresponding to the face images of the same person name are added, and if the maximum similarity after the addition is greater than the second threshold, the maximum similarity corresponding to the addition is taken. The name of the person as the name of the target face, otherwise the most similar face can be directly output.
  • another embodiment of the present invention further provides a face picture name recognition system, wherein the similar face picture recognition module 420 includes:
  • the feature storage module 421 is configured to extract features in the collected face images and store them in a preset face database.
  • the face in the picture is automatically detected for the collected face picture, and then the feature of the face is extracted and quantized into a high-dimensional vector.
  • Using high-dimensional vectors to represent faces can reduce the amount of data and facilitate subsequent similar face comparisons.
  • This will create a database containing the features of the collected face images.
  • For the face images corresponding to each person's name in the library collect as many images as possible of various expressions, angles or sizes, which is helpful for the subsequent recall and accuracy of face recognition: recognized faces There is no requirement for expressions, angles, poses, etc., and it is not necessary to input a picture as a frontal high definition image. Since various expressions, angles, and gestures of a human face are known in the database, various face images input can be recognized.
  • the feature comparison module 422 is configured to extract features of the picture including the target face and compare the features of the collected face pictures taken from the face database.
  • the face is automatically detected first, the feature of the face is extracted, and quantized into a high-dimensional vector; the vector and the library containing the picture of the target face
  • the feature high-dimensional vectors of the inner face images are compared, and the Euclidean distance is calculated, and the first N vectors closest to each other, that is, the first N similar faces are taken.
  • the face database is too large, it takes a long time to compare one by one, you can cluster the face in the library in advance, and then only compare with the face of the cluster, which can greatly shorten the comparison time; the specific comparison is high.
  • the dimensional feature vectors are compared, the Euclidean distance between the vectors is calculated, and the first N vectors closest to each other are taken.
  • the faces represented by these vectors are the faces most similar to the input face.
  • modules in the devices of the embodiments can be adaptively changed and placed in one or more devices different from the embodiment.
  • the modules or units or components of the embodiments may be combined into one module or unit or component, and further they may be divided into a plurality of sub-modules or sub-units or sub-components.
  • any combination of the features disclosed in the specification, including the accompanying claims, the abstract and the drawings, and any methods so disclosed, or All processes or units of the device are combined.
  • Each feature disclosed in this specification (including the accompanying claims, the abstract and the drawings) may be replaced by alternative features that provide the same, equivalent or similar purpose.
  • the various component embodiments of the present invention may be implemented in hardware, or in a software module running on one or more processors, or in a combination thereof.
  • a microprocessor or digital signal processor can be used in practice to implement in accordance with the present invention.
  • the face image of the example identifies some or all of the functionality of some or all of the components in the system.
  • the invention can also be implemented as a device or device program (e.g., a computer program and a computer program product) for performing some or all of the methods described herein.
  • a program implementing the invention may be stored on a computer readable medium or may be in the form of one or more signals.
  • Such signals may be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.
  • Figure 7 illustrates a face picture person identification system, such as a computing device, in accordance with the present invention.
  • the computing device conventionally includes a processor 710 and a computer program product or computer readable medium in the form of a memory 720.
  • Memory 720 can be an electronic memory such as a flash memory, EEPROM (Electrically Erasable Programmable Read Only Memory), EPROM, hard disk, or ROM.
  • Memory 720 has a memory space 730 for program code 731 for performing any of the method steps described above.
  • storage space 730 for program code may include various program code 731 for implementing various steps in the above methods, respectively.
  • the program code can be read from or written to one or more computer program products.
  • Such computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks.
  • Such a computer program product is typically a portable or fixed storage unit as described with reference to FIG.
  • the storage unit may have storage segments, storage spaces, and the like that are similarly arranged to memory 720 in the computing device of FIG.
  • the program code can be compressed, for example, in an appropriate form.
  • the storage unit includes computer readable code 731', ie, code readable by a processor, such as 710, that when executed by a computing device causes the computing device to perform each of the methods described above step.

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Abstract

L'invention concerne un procédé et un système de reconnaissance de nom d'image de visage humain, lesquels se rapportent au domaine technique des ordinateurs et ont principalement pour objectif de reconnaître avec précision des noms correspondant à des images comprenant des visages humains cibles. Le procédé comprend les étapes suivantes: définir des noms correspondants pour des images de visage humain recueillies (110); comparer les images comprenant des visages humains cibles aux images de visage humain recueillies, et reconnaître une ou plusieurs images de visage humain similaires aux images comprenant des visages humains cibles (120) ; et selon les noms correspondant à une ou plusieurs images de visage humain similaires respectivement, déterminer les noms des visages humains cibles (130). Les images de visage humain similaires sont reconnues sur la base d'une technologie de reconnaissance de visage humain, et les exigences concernant des expressions, des angles et analogue des visages humains cibles sont faibles et, par conséquent, différentes images de visage humain de la même personne correspondant au visage humain cible peuvent être reconnues plus facilement, et le nom du visage humain cible peut être déterminé plus facilement.
PCT/CN2015/085285 2014-07-28 2015-07-28 Procédé et système de reconnaissance de nom d'image de visage humain Ceased WO2016015621A1 (fr)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018091724A1 (fr) 2016-11-21 2018-05-24 Cureab Gmbh Anticorps anti-gp73 et immunoconjugués
CN111353364A (zh) * 2019-08-19 2020-06-30 深圳市鸿合创新信息技术有限责任公司 一种人脸动态识别方法及装置、电子设备

Families Citing this family (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104091164A (zh) * 2014-07-28 2014-10-08 北京奇虎科技有限公司 人脸图片人名识别方法和系统
CN104281842A (zh) * 2014-10-13 2015-01-14 北京奇虎科技有限公司 人脸图片人名识别方法和装置
CN104463177A (zh) * 2014-12-23 2015-03-25 北京奇虎科技有限公司 相似人脸图片获取方法和装置
US10489637B2 (en) 2014-12-23 2019-11-26 Beijing Qihoo Technology Company Limited Method and device for obtaining similar face images and face image information
CN105869235B (zh) * 2015-01-20 2019-08-30 阿里巴巴集团控股有限公司 一种安全门禁方法及系统
CN106201560A (zh) * 2015-04-30 2016-12-07 中国电信股份有限公司 一种匹配机顶盒用户界面的系统和方法
CN106326315A (zh) * 2015-07-07 2017-01-11 中兴通讯股份有限公司 图像文件管理方法和装置、以及通信录管理方法和装置
CN106649710A (zh) * 2016-12-20 2017-05-10 北京奇虎科技有限公司 图片推选方法、装置和移动终端
CN111382604B (zh) * 2018-12-27 2024-07-12 深圳光启空间技术有限公司 一种人脸识别方法及系统
WO2023141900A1 (fr) * 2022-01-27 2023-08-03 基建通(三亚)国际科技有限公司 Procédé et appareil permettant d'établir un graphe de connaissances de données d'image-de texte d'actualités, ainsi que dispositif et support
CN115905602A (zh) * 2022-11-03 2023-04-04 暨南大学 一种新闻人物识别方法及装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102867173A (zh) * 2012-08-28 2013-01-09 华南理工大学 一种人脸识别方法及其系统
CN102880634A (zh) * 2012-07-30 2013-01-16 成都西可科技有限公司 基于云的智能人脸识别检索方法
US20130216107A1 (en) * 2012-02-20 2013-08-22 Chih-Hsung Huang Method of surveillance by face recognition
US20130308836A1 (en) * 2012-05-18 2013-11-21 Primax Electronics Ltd. Photo image managing method and photo image managing system
CN104091164A (zh) * 2014-07-28 2014-10-08 北京奇虎科技有限公司 人脸图片人名识别方法和系统

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101739428B (zh) * 2008-11-10 2012-04-18 中国科学院计算技术研究所 一种对多媒体创建索引的方法
US8670597B2 (en) * 2009-08-07 2014-03-11 Google Inc. Facial recognition with social network aiding
US8644563B2 (en) * 2009-12-14 2014-02-04 Microsoft Corporation Recognition of faces using prior behavior
CN101833569A (zh) * 2010-04-08 2010-09-15 中国科学院自动化研究所 一种对电影人脸图像进行自动标识的方法
CN103218603B (zh) * 2013-04-03 2016-06-01 哈尔滨工业大学深圳研究生院 一种人脸自动标注方法及系统
CN103793697B (zh) * 2014-02-17 2018-05-01 北京旷视科技有限公司 一种人脸图像的身份标注方法及人脸身份识别方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130216107A1 (en) * 2012-02-20 2013-08-22 Chih-Hsung Huang Method of surveillance by face recognition
US20130308836A1 (en) * 2012-05-18 2013-11-21 Primax Electronics Ltd. Photo image managing method and photo image managing system
CN102880634A (zh) * 2012-07-30 2013-01-16 成都西可科技有限公司 基于云的智能人脸识别检索方法
CN102867173A (zh) * 2012-08-28 2013-01-09 华南理工大学 一种人脸识别方法及其系统
CN104091164A (zh) * 2014-07-28 2014-10-08 北京奇虎科技有限公司 人脸图片人名识别方法和系统

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018091724A1 (fr) 2016-11-21 2018-05-24 Cureab Gmbh Anticorps anti-gp73 et immunoconjugués
EP4015532A1 (fr) 2016-11-21 2022-06-22 cureab GmbH Anticorps et immunoconjugués anti-gp73
CN111353364A (zh) * 2019-08-19 2020-06-30 深圳市鸿合创新信息技术有限责任公司 一种人脸动态识别方法及装置、电子设备

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