WO2017202191A1 - Procédé et système de mesure de données faciales - Google Patents

Procédé et système de mesure de données faciales Download PDF

Info

Publication number
WO2017202191A1
WO2017202191A1 PCT/CN2017/083358 CN2017083358W WO2017202191A1 WO 2017202191 A1 WO2017202191 A1 WO 2017202191A1 CN 2017083358 W CN2017083358 W CN 2017083358W WO 2017202191 A1 WO2017202191 A1 WO 2017202191A1
Authority
WO
WIPO (PCT)
Prior art keywords
facial
feature point
facial feature
image
face
Prior art date
Application number
PCT/CN2017/083358
Other languages
English (en)
Chinese (zh)
Inventor
谭军
Original Assignee
广州帕克西软件开发有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 广州帕克西软件开发有限公司 filed Critical 广州帕克西软件开发有限公司
Publication of WO2017202191A1 publication Critical patent/WO2017202191A1/fr

Links

Images

Classifications

    • 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
    • G06V40/168—Feature extraction; Face representation
    • G06V40/169—Holistic features and representations, i.e. based on the facial image taken as a whole
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]

Definitions

  • the present invention relates to the field of information processing technologies, and more particularly to a method and system for measuring facial data.
  • Facial recognition is a biometric recognition technology based on human facial feature information. It is widely used in community security, network video surveillance, entry and exit management testing, employee attendance and home entertainment, etc. Fast and accurate measurement of facial data in an image or video stream is an important aspect of facial recognition technology.
  • the traditional measurement method of human head data is roughly divided into the following two types: the first is to directly measure the head data through the ruler, and the second is to obtain the three-dimensional model of the human head through the depth sensor, and then indirectly measure The data of the human head model.
  • the first measurement scheme can measure less data, and has strong professionalism, requires professional personnel to operate, the operation process is complicated, and the measurement process takes a long time; the second measurement scheme can measure more data, and has high precision and can be fully automatic. Measurement, but the measurement takes a long time, and the hardware cost is high, it is difficult to popularize, and it is widely used.
  • the technical problem to be solved by the present invention is to provide a face data measuring method and system for the above-mentioned defects of the human head data measurement in the prior art.
  • the technical solution adopted by the present invention to solve the above problems is to provide a method for measuring facial data, the method comprising the following steps:
  • the spatial coordinates of the calculated facial feature point group are output.
  • the step of generating a spatial coordinate of a set of facial feature point groups by using the face image through the mapping matrix comprises:
  • mapping the pixel coordinates of the facial feature point group through the mapping matrix to obtain spatial coordinates of the facial feature point group.
  • the method before the step of generating the spatial coordinates of the N sets of facial feature point groups by the face image through the mapping matrix, the method further includes:
  • the mapping matrix is generated according to the acquired calibration image.
  • the step of generating the mapping matrix according to the acquired calibration image includes:
  • the calibration image including a face and a credit card
  • the face in the calibration image is linearly scaled according to the actual distance of the outer corner of the eye to obtain a mapping matrix between the pixel coordinates of the face and the world coordinates.
  • N is greater than or equal to 6.
  • the invention also provides a facial data measuring system, the system comprising:
  • a generating module configured to generate a spatial coordinate of the N sets of facial feature point groups by using a preset mapping matrix, and N is a positive integer;
  • a calculation module configured to calculate an arithmetic mean value of spatial coordinates of the N sets of facial feature point groups
  • an output module configured to output the calculated spatial coordinates of the facial feature point group.
  • the generating module includes:
  • a first acquiring unit configured to acquire the facial image
  • a feature point acquiring unit configured to acquire the facial image by using a SURF algorithm Facial feature point group
  • a positioning unit configured to locate the facial feature point group by using an ASM algorithm to obtain pixel coordinates of the facial feature point group
  • a mapping unit configured to map pixel coordinates of the facial feature point group by using the mapping matrix to obtain spatial coordinates of the facial feature point group.
  • the system further includes:
  • a preset module configured to generate the mapping matrix according to the acquired calibration image.
  • the preset module includes:
  • a second acquiring unit configured to acquire the calibration image, where the calibration image includes a face and a credit card;
  • An identification unit configured to identify an actual distance of an outer corner of the eye according to an outer corner pixel distance in the face and a credit card pixel width of the credit card;
  • a calibration unit configured to linearly calibrate the face in the calibration image according to an actual distance of an outer corner of the eye to obtain a mapping matrix between pixel coordinates and world coordinates of the face.
  • N is greater than or equal to 6.
  • the present invention automatically generates facial data by generating a spatial coordinate of a plurality of sets of facial feature point groups through a preset mapping matrix, and accurate measurement of the facial data. High sex.
  • the facial data can be measured after obtaining the facial image through the camera, the cost is low, and it is easy to popularize, and can be widely applied to home entertainment and the like.
  • FIG. 1 is a flow chart of a first embodiment of a method for measuring facial data according to the present invention
  • FIG. 2 is a flow chart of generating spatial coordinates of a set of facial feature point groups by a preset mapping matrix according to a preferred embodiment of the present invention
  • FIG. 3 is a flow chart of a second embodiment of a method for measuring facial data according to the present invention.
  • FIG. 4 is a flowchart of generating a preset mapping matrix according to the acquired calibration image according to a preferred embodiment of the present invention
  • FIG. 5 is a schematic structural view of Embodiment 1 of the face data measuring system of the present invention.
  • FIG. 6 is a schematic structural diagram of a generation module in FIG. 5;
  • Fig. 7 is a schematic structural view of a second embodiment of the face data measuring system of the present invention.
  • the invention realizes automatic measurement of facial data by generating spatial coordinates of a plurality of sets of facial feature point groups by using a preset image through a preset mapping matrix, and the accuracy of the measured facial data is high.
  • the facial data can be measured after the facial image is acquired by the camera, which is low in cost and easy to popularize.
  • the measurement method includes the following steps:
  • step S102 the facial image is generated by using a preset mapping matrix to generate spatial coordinates of the N sets of facial feature point groups;
  • the step of generating spatial coordinates of a set of facial feature point groups by using a preset mapping matrix includes: acquiring a facial image in step S1021; the facial image may be a photo file directly captured by the camera Also, it is an image file that can be captured in a video file.
  • the facial image is obtained by the SURF (Speed-up robust features) algorithm, and the facial feature point group includes 86 feature points.
  • the facial feature point group is positioned by the ASM (Active Shape Model) algorithm to the pixel coordinates of the facial feature point group.
  • the pixel coordinates of the facial feature point group are mapped by the mapping matrix to obtain the spatial coordinates of the facial feature point group.
  • step S104 an arithmetic mean value of the spatial coordinates of the N sets of facial feature point groups is calculated; further, in the embodiment, N is greater than or equal to 6, by generating the spatial coordinates of the set of facial feature point groups as described above. In step, spatial coordinates of 6 sets of facial feature point groups are generated, so that the measured facial data is more accurate.
  • step S106 the calculated facial feature point space coordinate group is output.
  • the pass Through the above steps the face data is automatically measured, and the processing speed of the measurement is high, and the measurement of the face data can be completed in 1 second.
  • the method further includes: in step S100, generating a preset mapping matrix according to the acquired calibration image, specifically, as shown in FIG. 4, comprising: in step S1001, acquiring a calibration image, wherein the calibration image includes a face and a credit card; and in step S1002, identifying an actual distance of the outer corner of the eye based on the outer corner pixel distance in the face and the credit card pixel width of the credit card.
  • the actual standard credit card has an actual width of 54mm.
  • the face in the calibration image is linearly scaled according to the actual distance of the outer corner of the eye to obtain a mapping matrix between the pixel coordinates of the face and the world coordinates.
  • FIG. 5 is a schematic structural view of Embodiment 1 of the face data measuring system of the present invention.
  • the measurement system 100 includes a generation module 102, a calculation module 104, and an output module 106.
  • the generating module 102 is configured to generate a spatial coordinate of the N sets of facial feature point groups by using a preset mapping matrix. Where N is a positive integer.
  • the generating module 102 includes a first acquiring unit 1021 , a feature point acquiring unit 1022 , a positioning unit 1023 , and a mapping unit 1024 , where the first acquiring unit 1021 is configured to acquire a facial image;
  • the image file that is taken directly by the camera is also the image file that can be captured in the video file.
  • the feature point obtaining unit 1022 is configured to acquire the facial feature point group of the facial image by using the SURF algorithm; the facial feature point group includes 86 feature points.
  • the positioning unit 1023 is configured to locate the facial feature point group by the ASM algorithm to the pixel coordinates of the facial feature point group.
  • the mapping unit is configured to map the pixel coordinates of the facial feature point group through the mapping matrix to obtain the spatial coordinates of the facial feature point group.
  • the calculation module 104 is configured to calculate an arithmetic mean value of the spatial coordinates of the N sets of facial feature point groups; further, in the embodiment, N is greater than or equal to 6, and the spatial coordinates of the 6 sets of facial feature point groups are generated by the generating module 102. In this way, the measured facial data is more accurate.
  • the output module 106 is configured to output the calculated facial feature point space coordinate group.
  • the face data is automatically measured by the above steps, and the processing speed of the measurement is high, and the measurement of the face data can be completed in 1 second.
  • the difference from the facial data measurement system of the embodiment is that the measurement system 100 further includes a preset module 101, and the preset module 101 is configured to generate a preset mapping according to the acquired calibration image.
  • the matrix specifically, the preset module 101 includes a second obtaining unit 1011, an identifying unit 1012, and a calibrating unit 1013, wherein the second acquiring unit 1011 is configured to acquire a calibration image, wherein the calibration image includes a face and a credit card; and the identifying unit 1012 For identifying the actual distance of the outer corner of the eye based on the outer corner pixel distance in the face and the credit card pixel width of the credit card.
  • the actual standard credit card has an actual width of 54mm.
  • the calibration unit 1013 is configured to linearly calibrate the face in the calibration image according to the actual distance of the outer corner of the eye to obtain a mapping matrix between the pixel coordinates of the face and the world coordinates.

Abstract

La présente invention concerne un procédé de mesure de données faciales. Le procédé comprend les étapes suivantes consistant : à générer des coordonnées spatiales de N groupes de points de caractéristiques faciales d'une image faciale à l'aide d'une matrice de mappage prédéfinie, N étant un nombre entier positif; à calculer une moyenne arithmétique des coordonnées spatiales des N groupes de points de caractéristiques faciales; et à émettre les coordonnées spatiales calculées des groupes de points de caractéristiques faciales. L'invention concerne également un système destiné à être utilisé avec le procédé. En générant des coordonnées spatiales de multiples groupes de points de caractéristiques faciales d'une image faciale à l'aide d'une matrice de mappage prédéfinie, la présente invention réalise une mesure automatique de données faciales, les données faciales mesurées étant d'une grande précision. En outre, les données faciales peuvent être mesurées en utilisant seulement une image faciale obtenue par une caméra, de sorte que les coûts sont bas, et que le procédé peut être facilement utilisé.
PCT/CN2017/083358 2016-05-27 2017-05-05 Procédé et système de mesure de données faciales WO2017202191A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201610365422.8 2016-05-27
CN201610365422.8A CN106022281A (zh) 2016-05-27 2016-05-27 一种面部数据测量方法及系统

Publications (1)

Publication Number Publication Date
WO2017202191A1 true WO2017202191A1 (fr) 2017-11-30

Family

ID=57091410

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/083358 WO2017202191A1 (fr) 2016-05-27 2017-05-05 Procédé et système de mesure de données faciales

Country Status (2)

Country Link
CN (1) CN106022281A (fr)
WO (1) WO2017202191A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106022281A (zh) * 2016-05-27 2016-10-12 广州帕克西软件开发有限公司 一种面部数据测量方法及系统
CN106803065A (zh) * 2016-12-27 2017-06-06 广州帕克西软件开发有限公司 一种基于深度信息的瞳距测量方法以及系统

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101499132A (zh) * 2009-03-12 2009-08-05 广东药学院 一种人脸图像中特征点提取的三维变换搜索方法
CN101593365A (zh) * 2009-06-19 2009-12-02 电子科技大学 一种通用三维人脸模型的调整方法
CN106022281A (zh) * 2016-05-27 2016-10-12 广州帕克西软件开发有限公司 一种面部数据测量方法及系统

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN100346357C (zh) * 2006-01-19 2007-10-31 上海交通大学 用三维标记点直接进行三维模型变形的方法
CN101661617B (zh) * 2008-08-30 2011-11-02 华为终端有限公司 摄像机标定的方法及装置
KR101569268B1 (ko) * 2014-01-02 2015-11-13 아이리텍 잉크 얼굴 구성요소 거리를 이용한 홍채인식용 이미지 획득 장치 및 방법

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101499132A (zh) * 2009-03-12 2009-08-05 广东药学院 一种人脸图像中特征点提取的三维变换搜索方法
CN101593365A (zh) * 2009-06-19 2009-12-02 电子科技大学 一种通用三维人脸模型的调整方法
CN106022281A (zh) * 2016-05-27 2016-10-12 广州帕克西软件开发有限公司 一种面部数据测量方法及系统

Also Published As

Publication number Publication date
CN106022281A (zh) 2016-10-12

Similar Documents

Publication Publication Date Title
CN108549873B (zh) 三维人脸识别方法和三维人脸识别系统
EP3457078B1 (fr) Procédé et appareil de reconstruction tridimensionnelle à base de système de balayage tridimensionnel monoculaire
TWI566204B (zh) 三維物件識別技術
JP4883517B2 (ja) 三次元計測装置および三次元計測方法並びに三次元計測プログラム
US8988317B1 (en) Depth determination for light field images
US9875398B1 (en) System and method for face recognition with two-dimensional sensing modality
CN111914635B (zh) 人体测温方法、装置、系统和电子设备
US10424078B2 (en) Height measuring system and method
CN111339951A (zh) 体温测量方法、装置及系统
TW202121251A (zh) 活體檢測方法及裝置、儲存介質
CN105740778B (zh) 一种改进的三维人脸活体检测方法及其装置
CN104173054A (zh) 基于双目视觉技术的人体身高测量方法及其装置
CN104246793A (zh) 移动设备的三维脸部识别
JP6071002B2 (ja) 信頼度取得装置、信頼度取得方法および信頼度取得プログラム
CN111784778A (zh) 基于线性求解非线性优化的双目相机外参标定方法和系统
CN107924461A (zh) 用于多因素图像特征配准和跟踪的方法、电路、设备、系统及相关计算机可执行代码
TW201250608A (en) Image comparison system and method
CN111724496A (zh) 一种考勤方法、考勤装置及计算机可读存储介质
Silvester et al. A critical assessment of the potential for structure‐from‐motion photogrammetry to produce high fidelity 3D dental models
CN112001880B (zh) 一种平面构件的特征参数检测方法及装置
WO2017202191A1 (fr) Procédé et système de mesure de données faciales
CN111323125A (zh) 一种测温方法、装置及计算机存储介质、电子设备
CN113256611A (zh) 一种rgb-d配准精度测试方法及设备
CN109559347A (zh) 对象识别方法、装置、系统及存储介质
Betta et al. Face-based recognition techniques: proposals for the metrological characterization of global and feature-based approaches

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17802037

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS (EPO FORM 1205A DATED 08.04.2019)

122 Ep: pct application non-entry in european phase

Ref document number: 17802037

Country of ref document: EP

Kind code of ref document: A1