JP6616541B1 - A measurement method for displaying the degree of color of makeup glue on an image by AI judgment. - Google Patents

Abstract

The present invention provides a makeup image machine and a measuring method that express a makeup degree by an index display by AI judgment. SOLUTION: Entering the AI era, as a means to utilize AI for makeup, replace AI with image processing software or apps, computer-equipped personal computers with camera functions, smartphones or tablets, and makeup image machines. Therefore, instead of the conventional makeup method, it will be switched to an imaging device that makes makeup based on AI judgment. [Selection figure] None

Description

  The present invention relates to a makeup image machine and a measurement method that display an index of makeup paste based on AI judgment, and a method for determining makeup paste using AI as a high-tech technique, and appropriately applying makeup on the face skin in front of a conventional mirror. It is a technical field that improves the current situation.

  This is a technical reform that uses a makeup imager with an index to display the degree of makeup using AI and a measuring method, and uses a conventional mirror to determine the degree of skin beautification using an AI imager.

  According to the present invention, the makeup imager and the measuring method that display the degree of makeup paste by the AI judgment can measure the makeup paste by the judgment of AI, and the problems of the conventional makeup method can be solved.

  The present invention relates to a makeup image machine and a measurement method that display the degree of makeup paste as an index based on AI judgment devised in view of such a situation.

In the human skin according to claim 1, in the facial skin (ganki) image, utilizing artificial intelligence (referred to as "AI"), cortex of the facial skin (rough face) for each race, gender and age, and In the image of the facial part "chin, cheek, eyes, nose, under the nose and around the mouth", a beauty doctor (hereinafter referred to as "teacher") manipulates the image of each part into a circular shape, The cortex, that is, “normal skin / greasy skin / dry skin / mixed skin” for each cortex, the teacher data in which the index level indicating the degree of “cortex and water content” is entered for each gender and age , and the best makeup for each region multiple models color the cosmetic glue that was as "good" as the standard data, machine what you have defined the correct make and the teacher data you enter the color degree of "cosmetic glue" including lipstick in "letters and numbers" to artificially by learning is not the image processing software and the application, with a camera function PC or smart phone, or tablet to be built the software and application function, in the standard data for a plurality of models, based on the most appropriate data to itself, "good sites on the entire surface cosmetic glue the color face in the hairdresser or own The makeup image is registered , and the makeup image color is compared with “good data” at the time of each makeup, and the software image processing computer identifies the makeup color and measures the degree of makeup color by AI judgment. It is related with the measuring method which displays the color degree of makeup flakes of an image to an image by AI judgment .

3. In claim 1, according to claim 1, in making up by a beautician or himself, on the face skin image taken with a computer or smartphone with a camera function, or a tablet, in his / her gender and age and at a beauty salon / cosmetic store Display the entire screen in which the numerical data of the cortex and water content inspected are registered in the soft image, and based on the standard data of the model, save the image with the best makeup color on your image face as self-data, every time A function that displays an index of the degree of makeup color after makeup on the image and at the same time conveys the degree of makeup color by voice, and a face that is makeupd with the best color by the beautician or herself at every aging every year skin image is updated, compare and every cosmetic glue the updated image, the software of the image processing computer identifies the decorative color, to measure the degree of cosmetic color determination in AI Features, color degree of cosmetic glue relates measuring method of displaying an image in AI determination.

The makeup imager that measures the degree of makeup with the AI judgment of the present invention and displays it as an index is indispensable in the environment where human beings operate cultural life, everyone is conscious of beautiful skin when dressing as an appearance reaches the teenager In recent years, with the spread of personal computers, smartphones, and tablets, the software and application functions of the present invention are built into this medium so that you can easily check “Makeup paste including lipstick” anytime and anywhere. It is a makeup image machine, and its use place is a beauty salon, face-to-face sales of passenger cars, city buses, passenger planes (for each economy seat), passenger ships, offices, hotels, companies, public skin research institutions, commercial facilities, etc. It is possible to provide an environment that can provide makeup guidance at beauty hospitals and other facilities where you can check makeup glue in bathrooms and restrooms.

Race is a division of modern humans divided by trait features such as skeleton, skin, and hair.
Global races can be roughly divided by skin color and eyes and eyes, 1) white people (European race),
2) Black people (African race) and 3) Yellow people (Asian race).
Makeup is called “make-up” or “make-up” in English by dressing and decorating the face with white powder or lipstick. Makeup is the application of cosmetics to the surface of the neck, hands, feet, etc., centering on the human face, and beautifying it. It has a long history, and some people have been makeup since ancient times. In medieval Europe, it was no longer carried out as a “arrogance” of “the Seven Deadly Sins”. Makeup was also performed when various royal families appeared in public and at festivals. In addition, applying makeup on the body is called death makeup.

Definition of beautiful skin, skin care is 1) Remove dirt with soap. 2) Moisturize with lotion. 3) Enclose the moisture with cream. It means three processes (process).
From this, you can see that many skin problems have a fundamental problem with "drying", which makes it impossible to moisturize or confine. In addition, the following are commonly referred to as beautiful skin. 1) The skin should be tight and shiny. 2) Fine skin texture. 3) The skin is not dry and has moisture. 4) The skin is not dull. 5) There should be no pimples, spots, freckles, etc. Skin is composed of epidermis, dermis, and subcutaneous tissue. As the moisture in the epidermis and dermis increases, the skin becomes fresh.
In particular, when the moisture content of the stratum corneum of the epidermis increases skin tension. “Tension”, “Tsuya”, “Moisture”, etc. are closely related to the amount of moisture in this skin.

What kind of skin condition is beautiful skin, when you think "that person's skin is clean", where to judge, the condition of the skin that looks healthy and beautiful 1) Healthy and beautiful skin, you can feel fresh moisture The stratum corneum is in good condition and has sufficient moisture.・ Everything you can see with your eyes.・ When touched by hand, it is firm and soft. 2) It is well-colored, transparent, has good skin reincarnation, no stratification of the stratum corneum, and has good melanin metabolism and blood circulation. 3) A state where there is tension and elasticity, the work inside the skin is good, and collagen is sufficiently produced. Are the three major elements.

Make-up process to make your skin beautiful 1) Don't work hard in the morning face wash 2) Plenty water with lotion 3) Moisturize with milk or cream 4) Make sure to apply sunscreen regardless of the season 5) Makeup Base, control color & kneaded cheek, 6) Foundation / Skin foundation, 7) Finish with powder / Change the powder used for the skin you want to finish.
On the other hand, men also take care of their skin with moisturizing cream and mentholatum according to the cortex of each age, measure the human skin condition for the beauty of this application, input to the medium and makeup image machine and like women You can also manage beautiful skin.

Facial skin (read as Ganki) refers to the skin of a human face, and uses artificial intelligence (referred to as “AI”) in its image to determine the race, gender (say men and women), and age (10s and 20s)・ For each 30s-50s, 60s-90s), the cortex of the facial skin, that is, “normal skin / greasy skin / dry skin / mixed skin” image for each specialist (hereinafter referred to as “teacher”) Shows the degree of sebum and the amount of water for each type of cortex in the area surrounded by circular operations on the facial skin image of the facial part "chin, cheek, eye area, nose, under the nose and around the mouth" A model that defines the correct answer by artificially combining the teacher data with the index level and the skin data of the model with the best makeup for each cortical type, and the teacher data with the makeup index level. Create machine learning image processing software or application.

Cortex types 1) Normal skin refers to a healthy and ideal skin condition that is fully moisturized, textured and firm. 2) Oily skin refers to a state in which sebum is secreted excessively (more than necessary).
3) Dry skin literally means a state where the skin is not moist and moisture is insufficient.
Originally, the skin is horny as a barrier and retains moisture. Healthy skin is covered with a sebum screen made of a mixture of moisture such as sweat and sebum. 4) Mixed skin is a troublesome problem with both dry and oily skin. T zone and U zone (the four zones of skin that are active in cortical secretion in the T zone. Skin is divided into two types: “Hydraulic power” + “Natural metabolic power” + “Sebum secretion power” + “Toxin excretion power” is expressed in U zone).
In addition, AGE (an advanced glycation product) that accelerates aging has been reported to be a research result that “old people are more likely to die prematurely”. In other words, appearance aging and internal aging are closely related. The cause of this is what is called AGE (terminal glycation product), which is a “substance made by heating protein and sugar” and has strong toxicity and is a causative substance that promotes aging.

Among human skin, facial skin (ganki) images, using artificial intelligence (called “AI”), cortex of facial skin and face part “chin / cheek / eye / nose” by race, gender and age・ Cosmetics, that is, “normal skin / greasy skin / dry skin”, in the area surrounded by the circular operation of the image of each part by a beauty specialist (hereinafter referred to as “teacher”) on the image under the nose and around the mouth・ Mixed skin '' for each cortex and gender and teacher level data indicating the degree of “cortex and water content” for each age, and makeup with multiple models with the best makeup color for each region as “good” With image processing software and applications that use machine data to artificially create teacher data in which the color degree of `` makeup paste '' including lipstick is input as `` letters and numerical values '' and define correct answers as standard data A personal computer with camera function attached to the screen The software or application function is built into a tablet, smartphone, or tablet, and the color face that has been applied to the entire surface by the beautician or himself is based on the most suitable data in the standard data of multiple models. The makeup image is registered, and the makeup image color is compared with “good data” at the time of each makeup, and the software image processing computer identifies the makeup color and measures the degree of makeup color by AI judgment. A measuring method in which the AI determines the color level of makeup flakes and displays it on the image, and also when using a personal computer, smartphone, or tablet with a camera function in place on the screen side when applying makeup by the beautician or himself The whole screen where the photographed face skin image has its own gender and age, and numerical data of cortex and water content examined at a beauty salon / cosmetic store registered in the soft image Based on the model's standard data, the image with the best makeup color is stored as self-data on the face of the image itself, and the index of the degree of makeup color is displayed on the image after each makeup, and at the same time However, it is a function that conveys the degree of makeup, and for each aging every year, the facial skin image with the best color by the beautician or himself is updated, and the makeup image of each time is compared with the updated image, and the software image This is a measurement method in which a processing computer identifies makeup colors and measures the degree of makeup color based on the judgment of AI, and the judgment degree of makeup glue is judged by AI and displayed on an image.

Software is a term that is contrasted with hardware (physical machine) in the computer field, and refers to a computer program that performs some processing, and related documents. Software generally includes application software (application software or application) for specific tasks and tasks such as word processing software, and operating system (OS) that provides hardware management and basic processing to the application software and users. ) And other system software.
“Programming (computer)” and “software engineering” are software acts in the field of considering the application of software development, operation and maintenance in a form and quantity.

Artificial intelligence (AI) is a term that refers to a field of computer science that studies "intelligence" using the concept of "calculation" and a tool called "computer". "Technology that allows computers to perform intelligent actions such as language understanding, reasoning, and problem solving on behalf of humans" or "Research field on the design and implementation of intelligent information processing systems using computers" The
The expert said: To put it simply and without fear of misunderstanding, “What steps (algorithms) and what data (like intellectual actions (recognition, inference, language operation, creation, etc.) that could only be done by humans until now?” It is a field to study "if you have prior information and knowledge), can you execute it mechanically?"

What does AI mean to learn, the most typical answer is to learn how to distinguish between a “dog” and a “cat” (or a different creature) when you look at a picture, for example.
Even if human beings can understand at a glance, they do not know AI that has not learned anything, and if it is not taught, an appropriate answer cannot be derived. There are two main types of AI learning techniques. “Machine learning” and “day planning (deep learning)”, what are these technologies used to improve system efficiency and speed up data analysis?
Let's look at “machine learning” first. The technology has the feature that the AI analyzes the data and finds the rules and rules, rather than the developer (teacher) programming all the actions in advance. In other words, AI that allows you to perform specific tasks through training. For example, in the case of image recognition, AI is loaded with a large number of apple images with each tag tagged as “red apple” or “blue apple”. At that time, if AI is instructed to “distinguish by paying attention to the color”, AI will focus on the color of the apple even when an image of the apple that has not been analyzed yet appears. And they learn to distinguish between “red apples” and “blue apples”.

Deep planning is a further development of machine learning. The major difference from conventional machine learning is that the framework used to analyze information and data is different. This is a "neural network" created by imitating human nerves, and has made computer data analysis and learning powerful.
In order to know more in detail, we will analyze the image earlier and see how it distinguishes between “blue apples” or “red apples”. While machine learning had to be specified to focus on `` color '', in day planning, AI learns `` eye spots '' to distinguish and improves its performance . To put it another way, dayplaning learns where to look by looking at a lot of data, and it becomes smarter automatically without waiting for instructions from humans.
Although “machine learning” and “day planning” are AI mechanisms, it can be said that there is a difference in that they are promoting automation of function enhancement. In particular, it can be said that it has evolved in that it automatically finds the “eye spot (feature)” when distinguishing the objects of analysis.
It also incorporates an image recognition system (computer vision) that identifies objects and people appearing in images, a speech recognition system that listens to human speech and understands its contents, and a speech synthesis system that assembles words into voices. You can also tell the appropriate address by voice.

The machine learning workflow process (flow diagram) in the present application is created and operated in the following processes of “material” ⇒ (1), “creation” ⇒ (2) to (5), and “delivery operation” ⇒ (6).
(1) Preparation of data ・ “Consideration of what kind of data is used” ⇒ “Data collection”.
・ Data refers to beauty doctors such as university hospitals (beauty dermatologists), beauty clinics or major cosmetic manufacturers as “teachers”, race, gender and age (10's, 20's, 30's to 50's, 60's) Every 90th generation), the skin of the facial skin, ie, “normal skin / greasy skin / dry skin / mixed skin”, and facial areas “chin / cheek / eye / nose / under nose and around the mouth” ”Facial skin images (all color images) are manipulated in a circular shape and surrounded by the teacher data with the index level indicating the degree of sebum and water content for each cortex type, and the best for each cortex type Image data is created by artificially creating a sufficient amount of “teacher data” by inputting the makeup index level and inputting the makeup skin index level as the standard data of the skin of the model with makeup.

(2) Selection of method: Select “Machine learning“ method ⇒ learning method ⇒ algorithm ””.
・ Machine learning is one of the methods for creating artificial intelligence (AI). A normal machine moves according to a program written by humans. However, in machine learning, human beings do not create programs, and machines themselves learn from huge amounts of data and create models like judgment criteria. Then give yourself the answer. Learning for that is machine learning. An algorithm is a procedure that solves a specific problem and is clearly defined as a combination of simple calculations and operations, including mathematical solutions and calculation procedures, but IT (computer technology in the sense of information technology). In general, it refers to a set of processing procedures formulated so that computers can be given and executed in the form of programs.

(3) Preprocessing: “Selection of necessary data” ⇒ “Data running” ⇒ “Data expansion” ⇒ “Division of data for learning / evaluation”.
-Data running is the work of arranging extra time for data extraction and cleaning.
・ Data expansion is a technique to increase the number of data by many times by performing operations such as moving, rotating, enlarging, reducing, distorting and adding noise to the training data image.
・ Division of learning / evaluation data is different from training data in supervised learning.
The data is divided into 1), training data, 2), accuracy verification data, 3) and test data.

(4) Model training: “Changing hyperparameters” ⇒ “Learning”.
・ What are hyperparameters? ・ Trainers (instructors) handle the following three types of data when training (practice / training) a model.
・ Input data (training data) is a set of individual records (instances = case examples) that contain important features for machine learning problems. This data is used for training and a model is set up to make an accurate prediction for new instances of similar data.
• Model parameters are variables used to adapt the selected machine learning method to the data. For example, a deep neural network (DNN) is composed of many processing nodes (neurons = structural and functional units), and operations are defined for each node.
As data moves through the network, each node's operations are performed on the data. When training a DNN, based on the weights defined for each node, the magnitude of that node's impact in the final prediction is determined. This weight is an example of a model parameter. This is because it distinguishes between models that have parameters and other models of the same type that act on similar data.
• Hyperparameters are variables related to the training process itself when model parameters are variables and are adjusted by training with existing data.
For example, when setting up a deep neural network, you decide how many "hidden" layers of nodes to use between the input and output layers, and how many nodes to use for each layer.
These variables are not directly related to the training data. These are configuration variables. Another difference is that parameters change during a training job, but hyperparameters generally do not change during a job run.
Model parameters are optimized (called “tuning”) by the training process. Perform model operations on the data, compare the resulting predictions with the actual values for each data instance, evaluate accuracy, and adjust until the optimal value is found. The hyperparameter adjustments are similar, run the entire training job and examine and adjust the overall accuracy. In either case, make changes to the model structure and find the best combination to handle the problem.
・ What is learning? A large amount of image data is required for learning, and it is necessary for humans to set hyperparameters for how to learn such data. Usually, the learning is performed and a “trained model” is created. This is the phase of AI learning. And using the “learned model” to predict unknown events is a phase that uses AI, that is, a changing process.

(5) Model evaluation: “Inference” ⇒ “Repeat (2) to (5)” ⇒ “Choose the best model”.
・ Inference means inputting data into a trained model and receiving the result from that model as output. In other words, for supervised learning, check the accuracy of the model using accuracy verification data. Evaluate the performance (generalization performance) of the trained model for unknown test data, and the accuracy verification data may have a bias problem due to data habituation. Final check in a close context. (2) to (5) are repeated to select an optimum model.
(6) Delivery and operation to the market where the media is required “Deploy and operate the selected learned model in the production environment”
-Export the selected learned model in a form that can be executed in the operating environment and incorporate it into AI services and applications.

    As described above, in order to put AI to the practical use of highly accurate day planning, it is indispensable to prepare a sufficient amount of “teacher data” at the same time as speeding up the computer. Data is data that the computer learns, and the result determines the accuracy of day planning. Here, AI (machine learning) is applied to the teacher data in which the sebum, moisture content, and makeup index level of the application are input. Teacher data refers to a facial image that is a standard image (color image) of the skin of the beauty display image scanned by a specialist who is engaged in beauty and skin care, or a model with the best makeup. Image processing software or application that machine-learns the data that is processed and enclosed in the part that has been sewn, the amount of moisture and the index value of makeup glue, and is defined artificially by a specialist. Can be very labor intensive. Therefore, since efficient image processing is possible with the above learning system, the AI is a “calculation formula unnecessary” AI image in the teacher data.

  For speech, the recognition accuracy, which was considered a barrier to the spread of speech recognition by artificial intelligence (AI), has also been greatly improved by the advancement of machine learning technologies such as “day planning (deep learning)”. There is also data that exceeded. Therefore, speech recognition has reached a practical level from the research stage, and it is spreading to general households as a voice input and voice assistant service. In the future, the speech recognition technology used in various situations such as AI, dialogue with robots, and communication will be used in the “Makeup imager and measurement method for indexing the degree of makeup using AI judgment”.

A special doctor ("teacher") applies facial image of the facial skin for each age group, such as chin, cheek, eyes, nose,
The cortices are symbolized on the five areas of the face skin image under the nose and around the mouth that have been manipulated in a circular shape and expressed as “simple skin (A), oily skin (B), dry skin ( C) The mixed skin is displayed with “index level” indicating the degree of “sebum and moisture” for each of the four types (D). The ratio of oil (referred to as cortex) and moisture is shown numerically in the five parts surrounded by a circle on the face skin by the teacher. However, the moisture content of the skin decreases with age, but in the case of “adult healthy” skin, the average value of the moisture content is said to be “20% -30%”.
Display method of “index level” of “cortex and water content”, for example, “10s (2-8)”, “20s (3-7), 30s (4-6), 40s (5- 5), 50s (6-4), 60s (7-3), 70s (8-2), 80s (9-1), 90s (9-1) The data is input by the teacher.The face image taken with the camera is displayed on the face skin screen while “touching or tracing”, the cortex type is displayed as a symbol, and the “sebum and water content” is displayed at the exponent level for each symbol (cortex). For example, if the “index level” of “cortex and water content” is displayed as “A4-6” , let it be understood as “normal skin” as “40% oil” and “60% moisture” Compared with the standard data of ages, it will be used as a reference when performing “makeup”.
○ Too much oil can cause the skin to become sticky, but a certain amount is required. The presence of oil creates a natural thin barrier on the skin surface that prevents moisture from escaping into the skin.

Next, the display method of “Makeup index level” for each age, and the index of good or bad for “Degree of makeup”
Or, for example, the AI judgment of the degree of makeup color is expressed by three index levels of “bad (not good)”, “normally 2”, and “good is 3”. For example, you can take a picture of your makeup with the camera, display the face skin screen while “tracing” the face image, and circle the face skin image of the facial part “chin, cheek, eye area, nose, under the nose and around the mouth” The teacher inputs data of “standard data” for displaying the makeup glue in the part with the “color degree index of makeup glue” in the five parts that are manipulated in the shape and surrounded. Display the entire screen with your own cortex registered on the facial skin image taken with the camera when applying makeup, and register the best-skinned beautiful skin image as your own data based on the model's standard data・ After saving, compare your makeup glue with your own data at the time of makeup, and determine the degree of goodness or badness of the makeup glue color, the makeup degree color degree index level at the makeup stage or at the end of makeup, for example `` A system in which “Normal (2)” or “Good (3)” is displayed on the image with characters and numerical values, and can be known by voice.

The “personal computer” with a camera function of the present application refers to a personal computer, which is a computer that is occupied and used by an individual, and is abbreviated as “personal computer” or “PC”. “Smartphone” is a type of mobile phone equipped with an advanced mobile device operating system (OS) and is abbreviated as “smartphone”.
In short, a tablet is a plate-shaped digital device located between a smartphone and a personal computer, and is an item that is operated by touching the screen like a smartphone. It looks like a smartphone that has become larger, or a computer that has no keyboard. This is a device that combines the best parts of a smartphone and a PC, so it is very convenient if you use it well.

In this medium, the facial skin image of the facial part “jaw, cheek, eye area, nose, under the nose and around the mouth” is operated in a circular shape and surrounded by the cortex, that is, “normal skin / greasy skin / dry skin”・ For each type of `` mixed skin '', the index data indicating the level of sebum and moisture content is input, and the skin tone of the model with the best makeup for each cortex type is used as standard data, and the index level for makeup is input. With the purpose of facilitating selfies, personal computers, smartphones, or tablets with camera functions can be created by using image processing software or apps that have been artificially created with the teacher data and machine-defined and defined correct answers. It is a device with a camera attached to the image surface side, and this software and application functions are built into this medium. It can be used when checking makeup paste including lipstick between office work such as women (when taking a break).In particular, since smartphones always bring them, they can check makeup on work and travel.

The makeup image machine of the present application is a cortex, that is, a cortex, that is, a part of the face skin image described in the previous section, which is operated by a circular shape and surrounded by a facial skin image of the chin, cheek, eye area, nose, under the nose and around the mouth. For each type of “normal skin / greasy skin / dry skin / mixed skin”, teacher data with “index level indicating the degree of sebum and water content” and skin with the best makeup for each cortex type Attached to the image side (front surface) by image processing software or application using machine learning with the correct answer defined artificially together with the teacher data with “Makeup index level” entered as standard data Examples of the makeup image machine with a camera function include a makeup image machine with a supporting leg for holding on one hand, a fixed makeup image machine with a stand, a wall-mounted makeup image machine, and an elongated type makeup image machine. As a place of use, 1) A makeup image machine with a supporting leg for one-handed use is intended for general home use, a medium-sized screen that can be easily used by private use, and a device with an image like a conventional hand mirror. 2) The fixed-type cosmetic imager with a base is a device having a slightly larger screen and an image of a conventional mirror. 3) The wall-mounted make-up image machine has a slightly larger screen and is installed in a beauty salon + beauty salon, face-to-face sales of cosmetics, or a skin care research institution (company / public). 4) The slender type cosmetic image machine is integrated with the rear mirror of a car, and when the mirror is raised upward, the makeup image machine appears on the back of the mirror. When checking the makeup glue in the car, Equipment that can be used for such purposes. Similarly, it can be installed on intercity buses, passenger planes, passenger ships, passenger railways, etc.

In addition to the conventional software and application functions, the built-in AI image processing software or application functions that have been machine-learned about the functions of “computers”, “smartphones” or “tablets” with camera functions and makeup imagers A cosmetic condition in which AI added to the medium is used to analyze “makeup glue” on an image taken by a human face by AI judgment. A cosmetic operation such as the following is applied to a facial skin image taken by the camera. To be judged. 1) Analyze the current cortex for each facial part (chin, cheek, eye area, nose, under the nose and around the mouth). 2) Analyze the skin condition of the best makeup on each face part. 3) “Compare” the model's standard data with your own makeup glue to analyze the makeup level. 4) Or, in addition to the model standard data, the own best makeup glue is registered as data for each facial part. 5) The data of own best makeup paste is updated and registered for each age group.
6) When applying makeup, the entire face image of the face photographed by the camera is checked as needed to check the makeup state during makeup, and the degree is analyzed, and the index level and voice are used to indicate whether the makeup is good or bad. 7) Other functions that analyze and judge the makeup state required for makeup work. 8) A date can be automatically displayed on each makeup image and registered, saved, or deleted.

Regarding the operation of “PCs”, “smartphones” or “tablets” with camera functions, and cosmetic imagers, “PCs” and “smartphones” or “tablets” include the functions of conventional software or apps. However, the operation method is as follows: 1) Launch the camera app and select the “Face photo” icon (changes to the front camera on the screen), 2) Camera the face 3) In addition to the shooting function with the conventional camera, it is set to automatically capture the face skin shooting function. 4) Display whether the shot image is a moving image or a still image on the screen and select either 5) Press the shutter of the camera, 6) Trace the screen with your finger, you can scale up and down, left and right, make up by looking at the facial area, 7) Save and delete the skin condition as usual Control 8) Unlike the medium, the new “makeup image machine” equipped with a camera app that can capture high-quality images even on a medium such as a smartphone is a function only for facial skin photography. 1) Start the camera application, 2) Point the face to the camera, 3) Press the camera shutter, 4) Select the screen display whether the captured image is a video or still image, and 5) On the screen If you trace with your finger, you can scale up and down, left and right, and make up by looking at the facial part. 6) After that, the makeup status displays the symptom level on the screen at the judgment of AI and at the same time teaches the makeup by voice 7) After completion of the makeup work, touch “Close” to save and close the screen. However, when it is not necessary to save, touch “Delete” on the screen display.
The image quality is the same as or higher than that of digital cameras (all color images), enhanced functionality and a “makeup image machine” with an emphasis on convenience. The power source is connected to normal electricity or activated by a rechargeable battery. To do.

In the medium, makeup imager, and measuring method that the AI of the present invention judges and displays the degree of makeup glue as an index, teaches the makeup method and the quality of the makeup glue while looking at another person's face skin image by remote control It is also possible for beauty specialists at cosmetic companies and beauty salons to instruct how to make makeup individually or in groups (school students) on images.

  The functions of the skin image diagnostic device called “Vicia” used in beauty clinics and research institutes are 1) analysis of stains, 2) analysis of pores, 3) analysis of hidden spots, 4) porphyrin (the source of acne) 5) Analysis of wrinkles, 6) Analysis of texture, 7) Analysis of skin color, 7) Analysis of skin color, 8) Analysis of skin age, AI of the present invention The purpose and purpose of this medium are different from the media, makeup glue judging machine and measuring method that judge and display the degree of makeup glue with an index.

Conventionally, makeup is usually done in front of a hand mirror, a mirror or a wall-mounted mirror, but in the present invention, instead of a mirror, AI-equipped image processing software or application, a computer with a camera function or a smartphone or It is a "software or app" that allows you to make makeup while looking at the face skin image instead of a mirror on the tablet media and makeup image machine, and a new model with built-in functions in the media and skin image diagnostic device Manufacture requires a lot of labor. Therefore, the economy is revitalized by the increase in employment. Furthermore, the development of cosmetic imaging equipment that can be enjoyed and used by men and women (humans) around the world has industrial value and economic effects. Can be greatly expected.

Claims (2)

Of human skin, facial skin (ganki) images, using artificial intelligence (referred to as "AI"), by the race, gender and age, the skin of the facial skin (real face), and the facial part "chin, cheek, In the image of the eyes, nose, under the nose and around the mouth, the cosmetics doctor (hereinafter referred to as “teacher”) manipulates the image of each part into a circular shape and encloses the cortex, ie “normal skin / greasy” `` Skin, dry skin, mixed skin '' for each cortex, teacher data that inputs the index level indicating the degree of `` cortex and moisture content '' for each gender and age, and multiple models with the best makeup color for each part Image processing software that uses machine-learning to create a correct answer by artificially creating teacher data in which the color degree of “make-up paste” including lipstick is input as “characters and numerical values”. and by application, camera function with personal computers and smart Tofon, or tablet to be built the software and application function, in the standard data for a plurality of models, based on the most appropriate data to itself, to the site the entire surface of a beautician or own a cosmetic glue the color face a "good" The makeup image is registered , and the makeup image color is compared with “good data” at the time of each makeup. The software image processing computer identifies the makeup color and measures the degree of makeup color based on the judgment of AI. This is a measurement method that displays the color level of makeup glue on the image based on AI judgment . In claim 1, in making up by a beautician or himself, on the face skin image taken with a computer or smartphone with a camera function, or a tablet, the gender and age of the person, and the cortex and moisture content examined at the beauty salon / cosmetic store. numeric data to display the entire screen, which was registered in the software image, based on the standard data of the model, the image was cosmetic color to the best to its own image face is saved as a self-data, of cosmetic glue after each cosmetic The index of the degree of color is displayed on the image, and at the same time it is a function that conveys the degree of makeup with voice, and the face skin image with the best color by the beautician or yourself is updated every year , and every time cosmetic glue to compare and update the image, identifies the image processing computer decorative color software feature for measuring the degree of cosmetic color determination in AI, cosmetic glue Measurement method of displaying the image color degree in AI determination.