WO2019014814A1 - Procédé de détection quantitative de rides sur le front d'un visage humain, et terminal intelligent - Google Patents

Procédé de détection quantitative de rides sur le front d'un visage humain, et terminal intelligent Download PDF

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Publication number
WO2019014814A1
WO2019014814A1 PCT/CN2017/093191 CN2017093191W WO2019014814A1 WO 2019014814 A1 WO2019014814 A1 WO 2019014814A1 CN 2017093191 W CN2017093191 W CN 2017093191W WO 2019014814 A1 WO2019014814 A1 WO 2019014814A1
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WO
WIPO (PCT)
Prior art keywords
image
block set
face
black pixel
total area
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PCT/CN2017/093191
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English (en)
Chinese (zh)
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林丽梅
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深圳和而泰智能控制股份有限公司
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Priority to CN201780004161.XA priority Critical patent/CN108369644B/zh
Priority to PCT/CN2017/093191 priority patent/WO2019014814A1/fr
Publication of WO2019014814A1 publication Critical patent/WO2019014814A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • 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]
    • 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

Definitions

  • Face recognition technology is a technology for identifying and comparing facial visual feature information.
  • the research fields of face recognition technology include: identity recognition, expression recognition and gender recognition.
  • the face-lifting pattern is often used as an important facial visual feature information because of its different characteristics depending on the age or facial expression of the person.
  • face wrinkles can be detected by machine learning classification algorithms such as Bayesian algorithm and neural network algorithm.
  • the specific implementation process of the method for detecting facial wrinkles is: for a given input image that may include a human face, initially determining the approximate position of the face, extracting the outline of the inner face; and then using a sliding small window inside the face Slide on, and use the machine learning model to identify and classify the pictures in the window to determine whether they belong to wrinkles; if they belong to wrinkles, extract them; finally, combine the extracted pictures to obtain grayscale images of wrinkles.
  • the inventors have found that at least the following problems exist in the prior art: First, the inner face extracted in the prior art is a portion below the eyebrows, and does not include a forehead portion. Secondly, training machine learning models requires a large amount of tag data, and collecting tag data requires a lot of manpower and financial resources. Moreover, the prior art can only determine whether the block on the face is wrinkles and cannot quantify the severity of wrinkles. Therefore, even if the existing wrinkle detection technology is applied to the detection of face lift, it is not easy and convenient to quantitatively detect the severity of the raised lines in the face image.
  • an embodiment of the present application provides a method for quantitatively detecting a face raising pattern, including:
  • the image processing including binarization processing
  • the determining, according to the first valid block set, the lookup level of the face image including:
  • the determining, according to the first valid block set, the lookup level of the face image including:
  • the method further includes:
  • the acquiring the black pixel block set corresponding to the raised head in the fourth image, and recording the second active block set includes:
  • the determining, according to the first valid block set and the second valid block set, the headline level of the face image including:
  • the determining, according to the first valid block set and the second valid block set, the headline level of the face image including:
  • an embodiment of the present application provides a non-transitory computer readable storage medium, where the non-transitory computer readable storage medium stores computer executable instructions for causing a smart terminal to execute the above The method for quantitatively detecting a face raising pattern.
  • the embodiment of the present application further provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, when the program When the instruction is executed by the smart terminal, the smart terminal is caused to perform the method of quantitatively detecting the face lift pattern as described above.
  • An advantageous effect of the embodiment of the present application is that the method for quantitatively detecting a face-lifting head and the smart terminal provided by the embodiment of the present application intercepts an image corresponding to the forehead region of the face image by acquiring a face image, and recording a first image; then performing image processing including binarization processing on the first image to obtain a second image; and then acquiring a black pixel block set corresponding to the raised pattern in the second image, which is recorded as the first effective Block collection; finally determining the headline level of the face image according to the first effective block set, so that not only accurate recognition of the face image of the face image but also quantification of the severity thereof can be achieved.
  • the detection and quantitative detection methods are quick and convenient.
  • FIG. 4(a) is a schematic diagram showing an example of a second image provided by an embodiment of the present application
  • FIG. 4(b) is a schematic diagram showing another example of a second image provided by an embodiment of the present application
  • FIG. 5 is a schematic flow chart of another method for quantitatively detecting a face-lifting head according to an embodiment of the present application.
  • FIG. 6 is a schematic diagram of an example of a third image provided by an embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of an apparatus for quantitatively detecting a face raising pattern according to an embodiment of the present application.
  • the face-lifting pattern refers to the wrinkles on the forehead of the face. In people's ordinary facial expressions, it will not help Autonomously raises the eyebrows. In the long run, it will reduce and damage the muscles of the forehead. The elasticity of the subcutaneous fibrous tissue will gradually decrease, and the eyebrows will be habitually left behind when pressed into the forehead skin. Forming a face-lifting pattern, as the number of times the eyebrows are squeezed to the forehead skin increases, the number of face-lifting lines formed gradually increases, and gradually tends to become stubborn true wrinkles.
  • the method provided by the embodiment of the present application can be applied to any smart terminal having an image processing function.
  • the smart terminal includes but is not limited to: a beauty authentication machine, a personal computer, a tablet computer, a smart phone, a terminal server, and the like.
  • the smart terminal can include any suitable type of storage medium for storing data, such as a magnetic disk, a compact disc (CD-ROM), a read-only memory or a random access memory.
  • the smart terminal may also include one or more logical computing modules that perform any suitable type of function or operation in parallel, such as viewing a database, image processing, etc., in a single thread or multiple threads.
  • the logic operation module may be any suitable type of electronic circuit or chip-type electronic device capable of performing logical operation operations, such as a single core processor, a multi-core processor, a graphics processing unit (GPU), or the like.
  • the method for obtaining the coordinate parameters of the key points of the eyebrows in the face image may be: first, using a third-party toolkit, such as: dlib, performing face key points on the face image (eg, eyebrow key points, eyes) Positioning key points, facial contour key points, mouth key points, etc., and then selecting preset eyebrow key points and determining coordinate parameters thereof, the preset eyebrow key points may include one or more; or, The key points of the eyebrows preset in the face image are directly extracted, and the coordinate parameters thereof are obtained.
  • a third-party toolkit such as: dlib
  • performing face key points on the face image eg, eyebrow key points, eyes
  • Positioning key points, facial contour key points, mouth key points, etc. Positioning key points, facial contour key points, mouth key points, etc.
  • selecting preset eyebrow key points and determining coordinate parameters thereof the preset eyebrow key points may include one or more
  • the key points of the eyebrows preset in the face image are directly extracted, and the coordinate parameters thereof are obtained.
  • the texture feature of the wrinkles in the first image is mapped by binarizing the first image.
  • the binarization processing of the image may be performed by dividing the image into N windows according to a certain rule, and then dividing the pixels in the window according to a uniform threshold T for each of the N windows. For the two parts, binarization is performed.
  • the first image is binarized by adaptive threshold binarization processing, which is based on the neighborhood block of the pixel. The pixel value distribution determines the binarization threshold at the pixel location, and a better segmentation effect can be obtained.
  • the specific implementation manner of acquiring the black pixel block set corresponding to the raised head in the second image may be: determining a first reference width threshold u based on the second image; detecting the second All black pixel blocks in the image are recorded as a first block set; the noise block in the first block set is filtered according to the first reference width threshold u to obtain a black pixel block set corresponding to the raised head mark , recorded as the first valid block set.
  • the "head-up level” refers to the severity of the raised pattern of the detected person, which can be divided into multiple levels such as light, medium, and heavy, and each level corresponds to a corresponding one. Reference range.
  • the "reference value” is a parameter R that can determine the severity of the headline
  • the division standard of the reference value range corresponding to each level can be set by experimenting and observing the headstrip of a large number of face images. . For example, a batch of face images whose statistical degree of wrinkles looks very serious, whose reference value R is greater than or equal to a certain value, for example, R ⁇ a, the headline level of the face image satisfying R ⁇ a is determined as Heavy wrinkle rating.
  • the "filtering process” is to remove the texture features belonging to the fine lines in the first image, leaving only the texture features of the wrinkles. Therefore, it can be considered that the first image is an image including both fine lines and wrinkles, and the third image is an image including only wrinkles.
  • the head-up image of the face image is first divided into two categories according to the degree of the depth of the head-up pattern: fine lines and wrinkles, and then the level of the head-up pattern is subdivided in each category, and thus, in this embodiment
  • the "head-up pattern” may include, but is not limited to, no fine lines, no wrinkles, mild fine lines, moderate fine lines, severe fine lines, mild wrinkles, moderate wrinkles, and severe wrinkles.
  • the reference values Ra and Rb and the reference value range corresponding to each level may be set for the second image and the fourth image by experimenting and observing the headstrip of the plurality of face images in advance, and then based on the reference value. And its range determines the level of the raised face of the face image.
  • the manner in which the reference values Ra and Rb are set may be the same or different.
  • the total length of all black pixel blocks in the first/second effective block set in the horizontal direction may be directly used as the reference value Ra/Rb, and then combined with the values of the reference values Ra and Rb.
  • the head-up level of the face image is determined, and will not be enumerated here.
  • the first image, The second image, the third image, and the fourth image all have the same size.
  • the beneficial effects of the embodiment of the present application are: obtaining a third image by performing filtering processing on the first image, and performing image processing including binarization processing on the first image and the third image respectively to obtain a second image.
  • the image and the fourth image, and then determining the head-up level of the face image according to the second image and the fourth image, can divide the face-up pattern into two categories of fine lines and wrinkles, further refining the level of the face-lifting pattern. Improves the accuracy of quantitative detection of face-lifting lines.
  • FIG. 8 is a schematic structural diagram of a device for quantitatively detecting a face raising pattern according to an embodiment of the present application.
  • the device 8 includes:
  • An image obtaining unit 81 configured to acquire a face image
  • the image processing unit 83 is configured to perform image processing on the first image to obtain a second image, where the image processing includes a binarization process, or the image processing includes a binarization process and an erosion process;
  • the determining unit 85 is configured to determine a heading level of the face image according to the first valid block set.
  • apparatus 8 further includes filter processing unit 86 for performing a filtering process on the first image to obtain a third image.
  • the image processing unit 83 is further configured to: perform the binarization processing on the third image to obtain a fourth image.
  • the first image is further filtered by the filtering unit 86 to obtain a third image
  • the first image and the third image are respectively subjected to image processing in the image processing unit 83 to obtain a second image and a third image.
  • the first valid block set and the second effective block set are obtained by the edge detecting unit 84
  • the determining unit 85 determines the heading level of the face image according to the first effective block set and the second effective block set. It can divide the face-up pattern into two categories: fine lines and wrinkles, further refine the level of face-lifting lines, and improve the accuracy of quantitative detection of face-lifting lines.
  • the smart terminal 900 can be any type of electronic device, such as a mobile phone, a tablet computer, a beauty authentication device, etc. Referring to FIG. 9, the smart terminal 900 is shown in FIG. include:
  • processors 910 and memory 920 one processor 910 is taken as an example in FIG.
  • the memory 920 can include a storage program area and a storage data area, wherein the storage program area can store an operating system, an application required for at least one function; the storage data area can store an operation created according to the use of the device for quantitatively detecting the face-lifting of the face Data, etc.
  • memory 920 can include high speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device.
  • the memory 920 can optionally include a memory remotely located relative to the processor 910 that can be connected via a network to a device that quantitatively detects face lift. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • the embodiment of the present application further provides a non-transitory computer readable storage medium storing computer executable instructions executed by one or more processors, for example, A processor 910 in FIG. 9 may cause the one or more processors to perform the method of quantitatively detecting face lift in any of the above method embodiments, for example, performing the method steps 110 to 110 in FIG. 1 described above. 150, method steps 510 through 580 of FIG. 5, implement the functions of units 81-86 of FIG.

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Abstract

L'invention concerne un procédé de détection quantitative de rides sur le front d'un visage humain, et un terminal intelligent. Le procédé consiste à : acquérir une image faciale; capturer une image correspondant à une zone du front dans l'image faciale, et marquer l'image comme première image; traiter la première image pour obtenir une deuxième image, le traitement d'image comprenant un traitement de binarisation; acquérir un ensemble de blocs de pixels noirs, correspondant aux rides de front, dans la deuxième image, et marquer l'ensemble de blocs de pixels noirs comme premier ensemble de blocs valides; et en fonction du premier ensemble de blocs valides, déterminer le niveau des rides du front dans l'image faciale. Dans la solution technique mentionnée ci-dessus, les modes de réalisation de la présente invention permettent de détecter quantitativement l'importance des rides du front dans une image faciale, grâce à un procédé de détection quantitative rapide et pratique.
PCT/CN2017/093191 2017-07-17 2017-07-17 Procédé de détection quantitative de rides sur le front d'un visage humain, et terminal intelligent WO2019014814A1 (fr)

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CN201780004161.XA CN108369644B (zh) 2017-07-17 2017-07-17 一种定量检测人脸抬头纹的方法、智能终端和存储介质
PCT/CN2017/093191 WO2019014814A1 (fr) 2017-07-17 2017-07-17 Procédé de détection quantitative de rides sur le front d'un visage humain, et terminal intelligent

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CN111275610A (zh) * 2020-01-08 2020-06-12 杭州趣维科技有限公司 一种人脸变老图像处理方法及系统
CN112116523A (zh) * 2019-06-20 2020-12-22 腾讯科技（深圳）有限公司 针对人像头发的图像处理方法、装置、终端及介质
CN112712054A (zh) * 2021-01-14 2021-04-27 深圳艾摩米智能科技有限公司 脸部皱纹检测方法

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CN110110637A (zh) * 2019-04-25 2019-08-09 深圳市华嘉生物智能科技有限公司 一种人脸皮肤皱纹自动识别和皱纹严重程度自动分级的方法
CN112116523A (zh) * 2019-06-20 2020-12-22 腾讯科技（深圳）有限公司 针对人像头发的图像处理方法、装置、终端及介质
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