CN113576421A - Intelligent skin measuring method and device - Google Patents
Intelligent skin measuring method and device Download PDFInfo
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- CN113576421A CN113576421A CN202111058616.0A CN202111058616A CN113576421A CN 113576421 A CN113576421 A CN 113576421A CN 202111058616 A CN202111058616 A CN 202111058616A CN 113576421 A CN113576421 A CN 113576421A
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- skin
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- parameter data
- skin type
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
- A61B5/0077—Devices for viewing the surface of the body, e.g. camera, magnifying lens
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/44—Detecting, measuring or recording for evaluating the integumentary system, e.g. skin, hair or nails
- A61B5/441—Skin evaluation, e.g. for skin disorder diagnosis
Abstract
The invention discloses an intelligent skin measuring method, which comprises the following steps: s1, taking a picture to obtain a picture of the user; s2, calculating the skin type module parameter data of the user by using a deep learning skin algorithm for the obtained user photo; s3, dividing the data area into percentage ranges according to the size of the parameter data area of the skin type module; s4, comparing the skin type module parameter data of the current user with the corresponding percentage of the preset skin type, and analyzing the skin type grade of the user; s5, providing a skin care scheme and a skin care suggestion of the percentage corresponding to the skin type for the user according to the analyzed skin type module parameter data; s6, providing intelligent skin management service for users according to skin care schemes and skin care suggestions; the invention is more innovative and intelligent, and not only can meet the intelligent requirements of users, but also can enable the users to know the skin conditions and pay more attention to the skin problems.
Description
Technical Field
The invention relates to the technical field of skin measurement, in particular to an intelligent skin measurement method and device.
Background
The existing cosmetic mirror is a glass mirror surface, and a light supplement lamp or an LED mirror surface can be carried in a slightly better way, but the existing cosmetic mirror is only a common mirror when being seen on the whole, is only suitable for the simple requirement of a user for looking after the existing cosmetic mirror, and cannot meet the requirement of the user on intellectualization; the skin condition of the user cannot be analyzed, and an intelligent service skin care scheme cannot be provided; if a user wants to know the skin condition of the user, the user needs to use some skin measuring app to take a picture for detection or use equipment capable of measuring the skin, so that the skin measuring operation is inconvenient; it is desirable to provide a method and apparatus for implementing intelligent skin measurement, analyzing the skin condition of a user, and providing a skin care regimen for the user.
Disclosure of Invention
The invention aims to provide a method and a device which are convenient to operate, can realize intelligent skin measurement, can analyze the skin condition of a user and can provide a skin care scheme for the user.
The invention is realized by the following technical scheme:
an intelligent skin test method comprises the following steps:
step S1, taking a picture to obtain a picture of the user;
step S2, calculating the skin type module parameter data of the user by using a deep learning skin algorithm for the obtained user photo;
step S3, dividing the data area into percentage ranges according to the size of the parameter data area of the skin type module;
step S4, comparing the skin type module parameter data of the current user with the corresponding percentage of the preset skin type, and analyzing the skin type grade of the user;
step S5, according to the analyzed skin type module parameter data, providing a skin care proposal and a skin care suggestion of the percentage corresponding to the skin type for the user;
and step S6, providing intelligent skin management service for the user according to the skin care scheme and the skin care suggestion.
Further, in step S2, the skin algorithm calculating the skin type module parameter data of the user includes the following steps:
step S21, detecting the human face and carrying out human face skin segmentation treatment;
step S22, extracting skin type module parameter data information by using a Sobel operator, and judging by using a connected domain to obtain a final detection region; collecting a plurality of skin areas, and respectively calculating the skin areas and LAB color difference of data in preset skin types; wherein, L represents lightness, A represents red-green color difference, B represents blue-yellow color difference, and the color difference is minimum, namely the degree is minimum;
and step S23, performing speckle detection by using a high contrast retention algorithm, binarizing the gray-scale image after high contrast, and selecting a specified threshold value to obtain a detection area.
Further, the skin algorithm adopts an image semantic segmentation method, and adopts a U-net network structure and a PSPNet network structure.
Further, in step S22, the skin modules include a pore module, a stain module, a red blood cell module and an acne module.
Further, in the step S23, the skin type module includes a wrinkle module, a black eye module and a blackhead module.
Further, in step S1, a photo of the user is taken by using a smart phone or a digital camera.
Further, an intelligent skin measuring device comprises an intelligent mirror; the intelligent mirror is used for intelligently measuring the skin, analyzing the skin condition of a user and providing a skin care scheme for the user.
The invention has the beneficial effects that:
the method comprises the steps of obtaining a photo of a user by photographing; calculating the skin type module parameter data of the user by utilizing a deep learning skin algorithm for the obtained user photo; dividing the data area into percentage ranges according to the size of the skin type module parameter data area; comparing the skin type module parameter data of the current user with the corresponding percentage of the preset skin type, and analyzing the skin type grade of the user; providing a skin care scheme and a skin care suggestion of the percentage corresponding to the skin type for the user according to the analyzed skin type module parameter data; providing intelligent skin management service for users according to skin care schemes and skin care suggestions; the invention is more innovative and intelligent, and not only can meet the intelligent requirements of users, but also can enable the users to know the skin conditions and pay more attention to the skin problems.
Drawings
FIG. 1 is a block diagram of a process flow of an embodiment of the present invention.
Detailed Description
The invention will be described in detail with reference to the drawings and specific embodiments, which are illustrative of the invention and are not to be construed as limiting the invention.
It should be noted that the descriptions referring to "first" and "second" in the present invention are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
In the present invention, unless expressly stated or limited otherwise, the term "coupled" is to be interpreted broadly, e.g., "coupled" may be fixedly coupled, detachably coupled, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
An intelligent skin test method comprises the following steps:
step S1, taking a picture to obtain a picture of the user;
step S2, calculating the skin type module parameter data of the user by using a deep learning skin algorithm for the obtained user photo;
step S3, dividing the data area into percentage ranges according to the size of the parameter data area of the skin type module;
step S4, comparing the skin type module parameter data of the current user with the corresponding percentage of the preset skin type, and analyzing the skin type grade of the user;
step S5, according to the analyzed skin type module parameter data, providing a skin care proposal and a skin care suggestion of the percentage corresponding to the skin type for the user;
and step S6, providing intelligent skin management service for the user according to the skin care scheme and the skin care suggestion.
It should be noted that the occurrence of the deep learning framework reduces the threshold of entry, the user does not need to start coding from a complex neural network, the user can select an existing model according to needs, obtain model parameters through training, and the user can also add a layer on the basis of the existing model or select a classifier and an optimization algorithm which are needed by the user at the top. The fields in which the different frames are applicable are not completely consistent. In general, the deep learning framework provides a series of deep learning components, and when a new algorithm needs to be used, a user needs to define the new algorithm by himself and then the function interface of the deep learning framework is called to use the new algorithm customized by the user.
Specifically, in this embodiment, in step S2, the calculating the skin type module parameter data of the user by the skin algorithm includes the following steps:
step S21, detecting the human face and carrying out human face skin segmentation treatment;
step S22, extracting skin type module parameter data information by using a Sobel operator, and judging by using a connected domain to obtain a final detection region; collecting a plurality of skin areas, and respectively calculating the skin areas and LAB color difference of data in preset skin types; wherein, L represents lightness, A represents red-green color difference, B represents blue-yellow color difference, and the color difference is minimum, namely the degree is minimum;
and step S23, performing speckle detection by using a high contrast retention algorithm, binarizing the gray-scale image after high contrast, and selecting a specified threshold value to obtain a detection area.
Specifically, in the scheme of this embodiment, the skin algorithm adopts an image semantic segmentation method, and the skin algorithm adopts a U-net network structure and a PSPNet network structure.
Specifically, in this embodiment, in step S22, the skin module includes a pore module, a color spot module, a red blood streak module, and an acne module.
Specifically, in this embodiment, in step S23, the skin modules include a wrinkle module, a black eye module, and a blackhead module.
Specifically, in this embodiment, in step S1, a smart phone or a digital camera is used to take a picture to obtain a photo of the user.
Specifically, in the embodiment, the intelligent skin measuring device includes an intelligent mirror; the intelligent mirror is used for intelligently measuring the skin, analyzing the skin condition of a user and providing a skin care scheme for the user.
Specifically, referring to fig. 1, first, the present invention obtains a photo of a user by taking a picture; calculating the skin type module parameter data of the user by utilizing a deep learning skin algorithm for the obtained user photo; dividing the data area into percentage ranges according to the size of the skin type module parameter data area; comparing the skin type module parameter data of the current user with the corresponding percentage of the preset skin type, and analyzing the skin type grade of the user; providing a skin care scheme and a skin care suggestion of the percentage corresponding to the skin type for the user according to the analyzed skin type module parameter data; providing intelligent skin management service for users according to skin care schemes and skin care suggestions; the invention is more innovative and intelligent, and not only can meet the intelligent requirements of users, but also can enable the users to know the skin conditions and pay more attention to the skin problems.
The technical solutions provided by the embodiments of the present invention are described in detail above, and the principles and embodiments of the present invention are explained herein by using specific examples, and the descriptions of the embodiments are only used to help understanding the principles of the embodiments of the present invention; meanwhile, for a person skilled in the art, according to the embodiments of the present invention, there may be variations in the specific implementation manners and application ranges, and in summary, the content of the present description should not be construed as a limitation to the present invention.
Claims (7)
1. An intelligent skin test method is characterized by comprising the following steps:
step S1, taking a picture to obtain a picture of the user;
step S2, calculating the skin type module parameter data of the user by using a deep learning skin algorithm for the obtained user photo;
step S3, dividing the data area into percentage ranges according to the size of the parameter data area of the skin type module;
step S4, comparing the skin type module parameter data of the current user with the corresponding percentage of the preset skin type, and analyzing the skin type grade of the user;
step S5, according to the analyzed skin type module parameter data, providing a skin care proposal and a skin care suggestion of the percentage corresponding to the skin type for the user;
and step S6, providing intelligent skin management service for the user according to the skin care scheme and the skin care suggestion.
2. The intelligent skin test method according to claim 1, characterized in that: in step S2, the skin algorithm calculating the skin type module parameter data of the user includes the following steps:
step S21, detecting the human face and carrying out human face skin segmentation treatment;
step S22, extracting skin type module parameter data information by using a Sobel operator, and judging by using a connected domain to obtain a final detection region; collecting a plurality of skin areas, and respectively calculating the skin areas and LAB color difference of data in preset skin types; wherein, L represents lightness, A represents red-green color difference, B represents blue-yellow color difference, and the color difference is minimum, namely the degree is minimum;
and step S23, performing speckle detection by using a high contrast retention algorithm, binarizing the gray-scale image after high contrast, and selecting a specified threshold value to obtain a detection area.
3. The intelligent skin test method according to claim 1, characterized in that: the skin algorithm adopts an image semantic segmentation method, and adopts a U-net network structure and a PSPNet network structure.
4. The intelligent skin test method according to claim 1, characterized in that: in step S22, the skin model includes a pore model, a mottle model, a red blood silk model and an acne model.
5. The intelligent skin test method according to claim 1, characterized in that: in step S23, the skin type module includes a wrinkle module, a black eye module, and a blackhead module.
6. The intelligent skin test method according to claim 1, characterized in that: in step S1, a smart phone or a digital camera is used to take a picture to obtain a photo of the user.
7. An intelligent skin test device using the intelligent skin test method of any one of claims 1-5, characterized by: comprises an intelligent mirror; the intelligent mirror is used for intelligently measuring the skin, analyzing the skin condition of a user and providing a skin care scheme for the user.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150127898A (en) * | 2014-05-07 | 2015-11-18 | 주식회사 엘리샤코이 | Portable device for skin condition diagnosis and method for diagnosing and managing skin using the same |
| CN107437073A (en) * | 2017-07-19 | 2017-12-05 | 竹间智能科技（上海）有限公司 | Face skin quality analysis method and system based on deep learning with generation confrontation networking |
| CN108323203A (en) * | 2017-07-17 | 2018-07-24 | 深圳和而泰智能控制股份有限公司 | A kind of method, apparatus and intelligent terminal quantitatively detecting face skin quality parameter |
| CN111814520A (en) * | 2019-04-12 | 2020-10-23 | 虹软科技股份有限公司 | Skin type detection method, skin type grade classification method, and skin type detection device |
| CN113095147A (en) * | 2021-03-16 | 2021-07-09 | 深圳市雄帝科技股份有限公司 | Skin area detection method, system, image processing terminal and storage medium |
-
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- 2021-09-10 CN CN202111058616.0A patent/CN113576421A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150127898A (en) * | 2014-05-07 | 2015-11-18 | 주식회사 엘리샤코이 | Portable device for skin condition diagnosis and method for diagnosing and managing skin using the same |
| CN108323203A (en) * | 2017-07-17 | 2018-07-24 | 深圳和而泰智能控制股份有限公司 | A kind of method, apparatus and intelligent terminal quantitatively detecting face skin quality parameter |
| CN107437073A (en) * | 2017-07-19 | 2017-12-05 | 竹间智能科技（上海）有限公司 | Face skin quality analysis method and system based on deep learning with generation confrontation networking |
| CN111814520A (en) * | 2019-04-12 | 2020-10-23 | 虹软科技股份有限公司 | Skin type detection method, skin type grade classification method, and skin type detection device |
| CN113095147A (en) * | 2021-03-16 | 2021-07-09 | 深圳市雄帝科技股份有限公司 | Skin area detection method, system, image processing terminal and storage medium |
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