KR100993875B1 - A fuzzy based personal color diagnosis system and method - Google Patents

Abstract

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a fuzzy based personal color diagnosis system and a method for displaying a seasonal type based on a distance between a color defined by four seasons and an input skin color, hair color, and eye color. The present invention relates to a color diagnosis system and method, wherein the configuration of the present invention includes: a DB 30 storing model colors in which a season type is predefined; A color input unit 10 which receives a color of the human body's vagina; An operation control unit 20 for selecting a seasonal type based on a distance between a query color input from the color input unit 10 and a model color stored in the DB 30; A display unit (50) for displaying the selected season type according to a control signal of the calculation control unit (20); Characterized in that it comprises a.

According to the present invention, an objective and consistent personal color diagnosis can be received, and through the diagnosed color, a healthy, beautiful and more visible effect can be obtained. In addition, the public can be diagnosed personal color without the help of a color analyzer, and through the psychological synergistic effect it is also possible to treat with confidence and positive thinking and color psychology. In addition, it can be used for environment and products such as interior, interior design, electronics, furniture, jewelry processing, graphic advertisement, and clothing.

Personal color, diagnosis, season, fuzzy logic, distance, skin color, hair color, eye color

Description

Fuzzy-based personal color diagnostic system and its method {A FUZZY BASED PERSONAL COLOR DIAGNOSIS SYSTEM AND METHOD}

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a fuzzy based personal color diagnosis system and a method thereof, wherein a distance between a model color defined in four seasons and a color (skin color, hair color, and eye color) to be subjected to personal color diagnosis A personal color diagnostic system and method for displaying seasonal types based on the present invention.

Personal color refers to a unique human body color, such as the color of skin, hair, and eyes, which are represented by personal colors and physical characteristics.

Personal color diagnosis is to analyze colors based on personal color theory and to diagnose colors that suit you. This is a practical theory of color harmony analyzed based on the principle of color harmony and disharmony, and includes various theories such as basic principles of color, color analysis, color psychology, and color application. Personal color diagnosis is a color analysis method that produces images according to personal body color characteristics and types within the structure of the four season color system.

The concept of personal color is mentioned in Goethe's theory of color in 1810, where he finds that the background of light and darkness of color phenomena is the basic color of yellow and blue.

In 1928, Bauhaus colorologist Johannes Itten of Germany's comprehensive formative school taught his disciples that their preferred colors were their own colors, such as their color, skin color, hair color, and pupil color. Finding a connection and paying attention to the relationship between appearance and subjective color is known as the beginning of color analysis.

Since then, Faber Biren, a world-renowned color psychologist, has been highly praised for introducing the utility of color-based color theory into social life. Color defines each color as warm (cold) and cool (cold).

American colorist Robet Dorr (1905-1979) discovered the principle of color harmony in 1928. The color key system is based on the theory that human skin has two colors of tone, warm and cold. The theory categorized blue undertones and yellow undertones, arguing that there is a balance between colors of the same classification. This theory was brought to Japan in 1974 through the "THE COLOR KEY PROGRAM" used by Ameritone Paint Corporation.

Also, according to Bernice Kentner (1991), 2400 years ago Hippocrates classified people's personality patterns as gloomy (winter), cold (summer), cheerful (spring), and hasty (fall). It is used today in personality and psychological analysis, and he published "Color Me A Season" by linking Hippocrates' "personality theory" with the colors of human relations. Bernice Kentner classifies colors into 16 types from season colors. Absolute spring, absolute summer, absolute autumn, absolute winter, summer light summer), winter summer (snow white winter), summer spring (sun signed summer), spring summer (pastel spring), summer autumn (Indian summer ), Autumn and summer (gentlo-otom), winter and summer (white night light winter), spring and winter (strike spring), winter and autumn (Jetset winter), autumn and winter (bronzestone otom), spring and autumn ( Grorias Spring), Autumn and Spring (Golden Autumn) The analysis of 16 types.

Based on this theory, the most common personal color diagnosis method is to classify into four seasons of spring, summer, autumn, and winter type through natural body color (skin color, hair color, and pupil color).

The four seasons are classified into warm colors, cold colors, soft colors, and hard colors to represent representative colors as shown in FIG. 1.

1, warm and soft colors are classified as spring, cold and soft colors as summer, warm and hard colors as autumn, and cold and hard colors as winter.

A common method of diagnosing personal color by classifying into four seasons is diagnostic draping using a color diagnostic cloth (fiber).

This diagnostic draping method is to place the color diagnostic cloth close to the human body, and the color analyzer (or colorist) finds colors that match the subject, and diagnoses the seasonal type with the matching colors. Once the season type is determined, we consult with you about color coordination, accessories, hairstyles and colors to avoid.

However, the method of using the color diagnosis cloth is to diagnose the color analyzer to determine how suitable the color diagnosis cloth is to be diagnosed, and tends to depend on the subjective judgment of the color analyzer. In addition, there is a problem that a personal color diagnosis may appear differently for each color analyzer by such subjective judgment.

The personal color diagnosis method using the color diagnosis cloth by the color analyzer diagnoses the personal color based on the intrinsic color of the subject. Therefore, there is a problem that the diagnosed to be diagnosed with all cosmetics and other accessories (Accessory) removed.

In addition, the result of using the color diagnosis cloth is only one result, that is, only one selected season is diagnosed, and there is a problem in that it is not known how much it is related to other seasons.

In addition, in order to be diagnosed with a personal color requires the help of a color analyzer there is a problem that the public can not be diagnosed with the personal color through their own body color.

The present invention has been made to solve the above problems, and an object thereof is to provide a consistent personal color diagnosis.

It is also an object of the present invention to provide a system and method that provides for association with other undiagnosed seasons.

In addition, it is an object of the present invention to provide a personal color diagnosis system and method that can be diagnosed by simply inputting the human body color of the subject.

Another object of the present invention is to provide a system and a method for allowing a general public to easily diagnose his own color.

The configuration of the present invention according to an aspect for achieving the above object, DB (30) is stored in the model color in which the season type is predefined; A color input unit 10 which receives a color of the human body's vagina; An operation control unit 20 for selecting a seasonal type based on a distance between a query color input from the color input unit 10 and a model color stored in the DB 30; A display unit (50) for displaying the selected season type according to a control signal of the calculation control unit (20); Characterized in that it comprises a.

In addition, the method for diagnosing a personal color according to an aspect for achieving the object of the present invention, the step of receiving a color of the human vagina; Selecting a season type based on a distance between the input query color and the model color; Displaying the selected season type; Characterized in that it comprises a.

According to the present invention, an objective and consistent personal color diagnosis can be received, and a healthy and beautiful effect can be obtained through the diagnosed color.

In addition, the public can be diagnosed personally without the help of a color analyzer, it is also possible to treat with confidence and positive thinking and color psychology through the psychological synergistic effect with the personal color diagnosis.

In addition, it can be used for environment and products such as interior, interior design, electronics, furniture, jewelry processing, graphic advertisement, and clothing.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

2 is a conceptual diagram illustrating a model color and a query color according to the present invention.

The conceptual diagram of FIG. 2 expresses colors (model colors, query colors) in RGB, and model colors (S, M, A, W) defined seasonally in a three-dimensional space with each color expressed in RGB as an axis. A virtual space in which the color (I) of the subject's query is displayed.

In this case, the model color means a color in which the season type is predefined, and the color of the quality means the color of the human body of the subject to be diagnosed with the personal color.

In FIG. 2, for convenience, one model color is shown for each type of segment, but there may be a plurality of model colors. In addition, the season type is one selected from spring, summer (suMmer), autumn (Autumn), winter (Winter).

Since it is impossible to know which season the color of the input human body quality is compared with the model color, in the present invention, a fuzzy based fuzzy function that sets a set of model colors predefined by the same season type as one season set. It is defined as, and it is the configuration to select the season type according to the distance between the color of the season set and the quality defined by this fuzzy function.

That is, the present invention selects and displays the season type based on the distance between the model color in which the season type is defined in advance and the color of the question to be diagnosed with the personal color, which will be described later.

3 is a schematic block diagram of a fuzzy based personal color diagnostic system according to an embodiment of the present invention.

The fuzzy based personal color diagnosis system of the present invention includes a color input unit 10, an operation control unit 20, a DB 30, and a display unit 50.

The color input unit 10 receives the color of the human body's vagina. Here, the color of the human vagina may be configured to include at least one of skin color, hair color, and eye color.

In addition, the model color defined by the color of the human body and the seasonal type may be represented by one selected from HSB, Lab, RGB, and CMYK, but may be configured to input and convert HSB, Lab, RGB, and CMYK. .

Here, HSB is a color expression method representing Hue, Saturation, and Brightness, and the color is red at 0 degrees, yellow at 60 degrees, green at 120 degrees, cyan (cyan) at 180 degrees, There is blue at 240 degrees and magenta (red purple) at 300 degrees. Saturation is the amount of color in a particular color, usually expressed as a percentage, and the higher the saturation, the more intense the color. Brightness represents the amount of white in the color, expressed as a percentage.

The lab was published by the International Lighting Commission, the Commission Internationale d'Eclairage (CIE), where L stands for Luminosity, and a and b are named like the x and y axes in a mathematical graph, and a and b are Each has a value from -128 to 127. Lab is to express the colors of parts that do not overlap with each other due to the color difference between RGB and CMYK in one color system, and it is also used as an intermediate step when converting RGB to CMYK.

RGB is an abbreviation of red, green, and blue, which is mainly used for screen display, and can be selected within the range of 0 to 256 decimal numbers, respectively. If it is expressed in hexadecimal, it can be represented by numbers and letters consisting of 00 to FF.

CMYK stands for Cyan, Magenta, Yellow, and blacK. CMYK is expressed as a percentage.

As described above, there are various methods for expressing color, and a method and system for converting colors to and from each other belong to a known technology, and a technology for receiving a color of a human body using the known technology is provided in the present invention. The detailed description is omitted as can be easily carried out by those skilled in the art, but the present invention is not reduced or limited by the method of expressing color.

The color corresponding to the clicked portion of the color selection area unit 100 (refer to FIG. 6) displayed on the display unit 50 may be input as the color of the human body's query. Alternatively, a skin color input unit 11 (refer to FIG. 7), a hair color input unit 12 (refer to FIG. 7), and an eye color input unit 13 (refer to FIG. 7), which can be input as text, may be input as a numeric code. Here, the numeric code may be made of numbers, letters or a combination of numbers and letters.

Referring to FIG. 6, the color corresponding to the clicked portion of the color selection unit 100 displayed on the display unit 50 is input as the color of the human body. For example, the hook selection unit 101 displays the yellow base and the blue base as the horizontal axis and the brightness as the vertical axis, and receives the color of the part clicked with the mouse as the color of the human body to diagnose the personal color. .

The configuration that receives the color of the human body by the numeric code finds the same or similar color as the color of the human body in the color contrast table, inputs the numeric code of the color value displayed on the color contrast table, and inputs the input value into the human body's query. The color may be input to be diagnosed as a personal color.

The DB 30 stores model colors predefined as season types of spring, summer, autumn and winter, and stores colors already known as season types in the form of a training set. For example, in case of skin color (255, 233, 204), hair color (180, 154, 102), and eye color (126, 93, 50) expressed in RGB, the seasonal type is stored as Spring. .

In building the model color, the present invention can be divided into two embodiments.

The first is model color built from surveys and the second is model color built from professional color analysts.

First, the model color constructed through the survey is constructed through the questionnaire to select the body color of the subject and the color classified by the season in which the subject is preferred from the plurality of subjects.

That is, by surveying the color of the season type preferred by the model using a plurality of ordinary people as a model, it can be constructed as the human body color of the model corresponding to the surveyed season type. In one model color, a corresponding seasonal type is predefined.

The survey can be based on a wide range of age groups to select basic personal information, skin factor checks and harmonious colors for a large number of people.

The basic information may be configured to check the gender and age of the male and female, and the skin factor check may be configured to input the skin color, hair color, and pupil color of the pictograph.

The harmonic color may be configured to select a season from the first to fourth ranks in which preferred colors are collected among the colors classified by seasons.

Through this survey, the model color is constructed according to the season of the preferred colors and the relationship between the skin elements of the snowy characters.

Second, model colors constructed through color analyzers can be constructed based on data classified and classified by a plurality of color analyzers by judging seasonal types based on human body colors. That is, a plurality of color analyzers classify the season type based on the human body color of the model using a plurality of ordinary people as a model, and the model color is constructed based on the classified season types.

In this process, a plurality of color analyzers may be constructed by selecting only data diagnosed with the same result for one object, that is, one general person, and data diagnosed with different results for the same object may be excluded.

Therefore, a model color can be constructed based on the color of a human body of a seasonal type that is determined by a plurality of color analyzers to be equal to a plurality of ordinary people.

Here, the human body color means an intrinsic color of the human body, and means a skin color, a hair color, and an eye color of the human body.

The model color constructed as described above is classified into a model color stored as a spring color, a model color stored as a summer color, a model color stored as an autumn color, and a model color stored as a winter color for each season. 30).

That is, the present invention stores each model color stored as a seasonal type as a fuzzy set, and shows the seasonal type, matching color, and belonging degree based on the distance between the fuzzy set and the color of the query.

The calculation control unit 20 calculates the season type, the recommendation color, and the degree of belonging based on the distance between the color of the human body input from the color input unit 10 and the model color defined as the season type in the DB 30. Calculate.

Seasonal type is one selected from spring, summer, autumn and winter, the recommended color may be composed of a set of colors that match the color of the human body.

Here, the degree of belonging indicates how much the color of the human vagina belongs to the seasonal set of model colors in which the season type is predefined. That is, the degree of belonging indicates how far in the season the color of the vagina is in spring, summer, autumn and winter, and the shorter the relative distance from the season, the higher the degree of belonging and can be expressed as a percentage.

In addition, the operation control unit 20 controls the display unit 50 to display the selected season type, the recommended color, and the calculated degree of belonging.

The display unit 50 displays the diagnosis season and the degree of belonging calculated by the control signal from the calculation control unit 20.

The distance between the query color and the specific season may be calculated by the calculation control unit 20 using Equation-1 below.

식-1

Equation-1

here,

D _t (x): the color of the vagina and the distance from a particular season.

dist (x, c _t ⁱ ): The distance between the model color (c _t ⁱ ) stored in the DB 30 and the input color of the human body (x).

min: The function to get the minimum value.

{c _t ⁱ | t∈T} ⊂ C: A subset of one season (t) selected from the stored model colors.

T: Seasons (Spring, Summer, Autumn, Winter).

t: one season selected from seasons.

C: A set of all model colors stored in the DB 30.

The model color defined as the spring season type, the model color defined as the summer season type, the model color defined as the autumn season type, and the model color defined as the winter season type and the quality of the input body through Equation-1. The distance having the minimum value for each season is selected based on the distance from the color (x).

That is, as a result of equation-1

D _Spring (color of quality) = {distance of spring season type},

D _ice (color of quality) = {distance of summer seasonal types},

D _Fall (quality of color) = {distance of autumn season type} and

D _Winter (quality of color) = {distance of winter season types} is selected.

The distance between the query color and the model color (dist (x, c _t ⁱ )) is to calculate the distance between two points in multidimensional space.

식-2

Equation-2

here,

m: Skin color, hair color, eye color

n: R, G, B

a _m : Means weight.

The weight (a _m ) is a value that can adjust the color of the quality of the body parts (skin color, hair color, eye color) to be weighted. That is, the present invention may be configured to represent the color application intensity of which color is more weighted in the color of the query. In the present invention, it may be configured to be calculated by varying the weight, which is the application intensity among skin color, hair color, and eye color. By adjusting the weight, the weight occupying the color of each human body for each part of the human body can be adjusted differently.

The most important factor in the diagnosis of personal color is skin color. This is because it represents the largest area among the color of the human vagina, followed by the hair color. Lastly, the diagnosis tends to be made in the order of pupil color.

In the present invention, it is characterized in that it is calculated by adjusting the weight (a _m ) differently for each body part in the distance calculation.

That is, in the distance calculation, the weight (a _m ) is multiplied for each part of the human body to apply a high specific gravity to the color of the specific human part may be configured to display the season type, recommended color, degree of belonging.

If three kinds of colors represented by skin color, hair color, and eye color are assumed to be three dimensions, and are assumed to be three dimensions each represented by RGB, the color becomes nine dimensions. Therefore, in Equation 2, the distance between the model color and the color of the query can be calculated as the Euclidean distance between two points in the 9th dimension.

The degree of belonging can be calculated by dividing the distance reciprocal between the input color and the season type by the sum of the whole reciprocals.

This method is calculated through Equation-3.

식-3

Equation-3

here,

p (t ^* | C) is the membership of one selected seasonal type (t ^* ).

In calculating the membership, the numerator is the inverse of the distance to the selected season, and the denominator is the sum of the inverses of the distance for all passages.

For example, if you want to calculate the membership of spring, the numerator is 1 / D _spring (x), and the denominator is 1 / D _spring (x) + 1 / D _summer (x) + 1 / D _autumn (x ) + 1 / D _winter (x)

The value of this calculation, p (t ^* | C), is calculated to a value below the decimal point and can be expressed as a percentage (%).

3A illustrates a configuration diagram according to an embodiment of the present invention, and illustrates a configuration of receiving a query color using a camera and an image sensor.

The configuration that receives the color of the query using the camera 110 captures the human body with the camera 110, displays the photographed image on the display unit 50, and clicks a portion of the human body that is clicked on the displayed image. It may be configured to receive the input in the query color.

In this case, the image photographed by the camera 110 may be photographed differently depending on the illuminance, so that the illuminance required for the photographing may be provided in advance.

In addition, the color input unit 10 of the present invention may be configured to receive a query color from the color sensor 120 for sensing the color.

The color detection sensor 120 is a sensor that detects the reflected light by irradiating light to the object at the receiving unit and converts the detected light amount into an electrical signal and outputs the electrical signal.

Therefore, FIG. 3A is a configuration of measuring the color of the human body using the color sensor 120 and receiving a signal output from the color sensor 120 as the color of the human body.

The color detection sensor 120 includes an RGB sensor, and the like, and the color detection sensor 120 belongs to a known technology, and a detailed description thereof will be omitted.

The configuration for receiving the color of the human body according to FIG. 3A is a configuration for receiving a portion of the color of the human body, and the input color may be a lighter or darker portion than the color of the color of the entire human body.

That is, the color received by the click of the mouse in the image captured by the camera 110 may be limited to the portion where the mouse is clicked.

Therefore, the color may be input by a mouse click at least two times, and the average value of the input color may be input as the query color.

In addition, the configuration that receives the color of the query to the color sensor 120 may also be configured to receive the average value as the color of the query by measuring the portion of the human body more than two times.

The configuration of receiving the color of the human body by using the camera 110 may be a service through the Internet. Receiving a photograph of the human body of the subject to be transmitted to the Internet, and displays the transmitted image on the display unit 50 to perform a personal color diagnosis and the result of the diagnosis through the Internet.

And the configuration to receive the color of the human body by using the color sensor 120 may be provided in parallel with the configuration to receive the color of the human body with a text input window. That is, it may be configured to diagnose the personal color by inputting the color output from the color sensor 120 to the text input window.

4 is a flowchart illustrating a personal color diagnosis method using fuzzy logic according to the present invention.

The color of the human body is input (S1).

The season type is selected based on the distance between the input query color in step S1 and the model color stored in the DB 30 (S2).

The selected season type in step S2 is displayed (S3).

Step S1 may be configured to receive the color of the portion clicked on by the color selector 100 (refer to FIG. 6) displayed on the display unit 50. This configuration comprises the steps of displaying the color selection unit 100 on the display unit 50; Receiving a color of the human body quality by clicking with the mouse in the color selection unit 100; As shown in FIG.

In addition, the display unit 50 may be configured to receive a numeric code by including a skin color input unit 11, a hair color input unit 12, and an eye color input unit 13 that may receive a numeric code. This configuration comprises the steps of displaying the color selection unit 100 on the display unit 50; Inputting a numeric code into the color selector (100); Receiving the input numeric code as a color of a human body's query; As shown in FIG.

In addition, it may be configured to receive the color of the human body by clicking the mouse in the image displayed on the display unit 50. This configuration comprises the steps of loading a photographed image; Displaying the loaded image on a display unit (50); Receiving a color of a human body by clicking a mouse on an image displayed on the display unit 50; As shown in FIG.

Step S2 may include selecting a recommendation color or calculating a degree of belonging based on the distance between the query color and the model color. In step S2, the step S3 may be displayed such that the selected recommendation color is displayed or the calculated degree of belonging is displayed. Can be done.

That is, the step S2 includes selecting or calculating the season type, the recommended color and the degree of belonging based on the distance, and the step S3 includes the process of displaying the season type, the recommended color, and the calculated degree of belonging selected in the step S2. do.

The step of selecting a season type based on the distance in step S2 includes the steps of calculating the distance between the query color and the model color and the season type to which the model color corresponding to the minimum value belongs in the calculated distance. Selecting the type.

In the step of calculating the distance, the distance is calculated in a multi-dimensional space between the model color classified by the season and the color of the query, and the season type is selected as the season type to which the model color corresponding to the distance of the minimum value belongs.

In addition, the step of selecting a recommendation color based on the distance may be stored in the DB (30) to match the season type, respectively, select the season type belonging to the model color selected as the minimum distance as the season type, the selected season The recommendation color corresponding to the type may be configured to be selected.

The calculating of the degree of belonging in step S2 may include calculating a distance between a query color and a model color for each season; Selecting a distance of a minimum value for each season from a distance calculated for each season; Calculating a degree of belonging by dividing the distance reciprocal of the minimum value selected for each season by the sum of the distance reciprocals of the minimum seasonal values; It may be made of.

The degree of belonging can be calculated by Equation-3.

In addition, the color of the human body may be input using the camera 110 as shown in FIG. 3A. In this case, a method of receiving an input using the camera 110 will be described.

5 is a flowchart illustrating a color input of a human body using the camera in step S1.

The human body image photographed by the camera is loaded (S21).

The captured human body image in step S21 is displayed on the display unit 50 (S22).

The color of the portion clicked by the mouse in the image displayed on the display unit 50 in step S22 is input as a query color (S23).

In operation S21, an image in which the skin, the hair, and the eyes of the human body are individually photographed may be loaded, or an image in which the skin, the hair, and the eye region is enlarged and edited may be loaded from the entire image.

In addition, one image including all skin, hair, and eyes may be displayed on the display unit 50, and the displayed image may be classified into skin color, hair color, and pupil color to be input.

In operation S21, the color of the human body may be input using the color sensor 120 measuring the color value. This step includes receiving the color measured by the color sensor 120 as the color of the human body; It includes.

Displaying the captured human body image on the display unit 50, and receiving the color of the human body by the color detection sensor 120 for measuring the color value or the step of receiving the color of the human body with a click of the mouse in the displayed image. The step may be configured to measure the color at least twice and receive the average value as the query color.

That is, the step of receiving the color of the human body by clicking the mouse on the displayed image may include repeating the mouse click at least two times on the image displayed on the display unit 50; Calculating an average value of the colors clicked with the mouse; Receiving the calculated average value as a color of a human body's vagina; It may be made of.

And receiving the color measured by the color sensor 120 to measure the color value as the color of the human body quality is the step of measuring the color of the human body at least two times with the color sensor 120; Calculating an average value of the measured color of the human body; Receiving the calculated average value as a color of the human vagina; . ≪ / RTI >

In the present invention, the model color defined as the season stored in the DB 30 uses a non parametric estimation method using a nearest neighbor algorithm. There is a decision tree modeling method similar to this non-parametric prediction method, but the decision tree modeling method is intuitive and easy to explain, while the non-parametric estimation method is more versatile. There is an advantage.

In order to predict the attribute value of a new entity, the nearest neighbor algorithm (NN algorithm) is to predict a value by selecting a similar one among already stored entities. In other words, it is a classification algorithm in which known objects are stored in memory in the form of a training set, and then similar objects are selected to predict new object values according to the selected object values. This algorithm has already proven its usefulness in various fields and is widely used in machine learning field recently.

6 to 8 show screens according to an embodiment of the present invention.

FIG. 6 is a screen for diagnosing personal color by receiving the clicked portion of the color selection unit as the color of the human body as an embodiment of the present invention. As shown in FIG. 6, the skin color and the hair of the display unit 50 are shown in FIG. A color selector 100 for selecting a color by clicking on a color and an eye color with a mouse, a diagnostic button 200 for executing a personal color diagnosis with respect to an input query color, and a result belonging degree of a personal color diagnosis. Affiliation degree display unit 300, the season type display unit 310 for the diagnosed season, the recommended color display unit 320 for displaying a color suitable for the diagnosed season type, and the color to determine the weight of the human body color The application strength selector 330 and a gender / age selector 340 for selecting gender and age of men and women.

Here, the recommendation colors displayed on the recommendation color display unit 320 may be classified according to gender and age of men and women and displayed based on data input to the gender / age selection unit 340.

The color selector 100 includes a skin color selector 101 displayed with a yellow axis and a blue base as a horizontal axis and brightness as a vertical axis, a hair display unit 102 and a pupil color display 103 displayed with a brightness as a vertical axis. It includes.

The color application strength selection unit 330 for determining the weight may be displayed as 'weak', 'medium', 'strong' and the like. Therefore, the weight may be configured to be diagnosed as a personal color by adjusting the weight to 'strong', 'middle', and 'weak', and may be configured to allow personal color to be diagnosed by emphasizing a selected part of the human body. have.

The DB 30 stores the model color defined as the season by dividing the gender and the age, and the color defined as the season corresponding to the gender and age selected by the gender / age selection unit 340 and the color of the input human body. It can be configured to be diagnosed personal color through the distance calculation of. This age range may be applied by subdividing into 5 year old units, 10 year old units, and the like.

According to FIG. 6, the display unit 50 is configured to display a color input unit 10 including a color selector 100 that receives a color of a human body.

7 illustrates a screen of an embodiment for diagnosing personal color by receiving a color of a human body with a numeric code.

The color contrast table shows the color and its numeric code. In this color contrast table, a color similar to the color of the human body is selected and the numeric code displayed on the color is input as the color of the human body.

According to the accompanying FIG. 7, the display unit 50 is configured to display a color input unit 10 including a color selector 100 for selecting and inputting a color, and inputs a numeric code to the color selector 100. It is provided with a text input window that can receive a numeric code in the text input window, the input of the numeric code as the color of the human body to receive the diagnosis of personal color.

The text input window receiving the numeric code may include a skin color input unit 11, a hair color input unit 12, and a pupil color input unit 13.

Here, the numeric code may be made of a number or a combination of numbers and letters, and may be configured to be interchangeable depending on a color expression method.

In addition, the color expression units 111, 121, and 131 may be displayed to determine which color is displayed on the screen.

The color shown in one embodiment is a color expressed in RGB, and the method of expressing the color is various, such as RGB, CMYK, Lab, and HSB.

8 is a screen of an embodiment in which an image captured by a camera is displayed on the display unit 50 to receive a clicked portion as a color of a human body to diagnose a personal color.

According to FIG. 8, the color selection unit 100 that receives the color of the human body by clicking the mouse on the displayed image is configured to be displayed on the display unit 50.

In the color selector 100 displayed on the display unit 50, a skin image 14, a hair image 15, and a pupil image 16 are displayed, and the images of the human body can be input by clicking a mouse on the images. It can be configured to be.

These image pictures may consist of a single picture including skin, hair and eyes or a partially enlarged picture of skin, hair and eyes, and a skin color image button 141 and a hair color image button 151 for selecting an image. ) And the pupil image button 161 may be provided.

In the present invention, the color input unit 10 including the color selection unit 100 is configured to be displayed on the display unit 50 to receive the color of the human body. Although a description has been given of a configuration in which a color of a human body is input by clicking or inputting a numeric code, the color input unit 10 can be easily changed and configured by a person skilled in the art to which the present invention pertains. The present invention is not limited or limited by the accompanying drawings.

On the other hand, the personal color diagnostic method according to the present invention can be created by a computer program. The codes and code segments that make up the program can be easily deduced by a computer programmer in the field. The program is also stored in a computer readable media, and read and executed by a computer to implement a personal color diagnostic method. The information storage medium includes a magnetic recording medium, an optical recording medium, a flash memory and a carrier wave medium.

It will be appreciated that the present invention can be implemented in a modified form within the scope of the claims of the present invention and the specific details for the practice of the present invention.

1 is a representative color separated into four season types.

2 is a conceptual diagram illustrating a model color and a query color according to the present invention.

3 is a schematic block diagram of a personal color diagnostic system using fuzzy logic according to an embodiment of the present invention.

3A is a block diagram illustrating a configuration for receiving a query color by using a camera and an image sensor.

4 is a flowchart of a personal color diagnosis method using fuzzy logic according to an embodiment of the present invention.

5 is a flow chart for receiving the color of the human body using a camera according to an embodiment of the present invention.

6 is a screen according to an embodiment of the present invention, a screen for diagnosing personal color by receiving a color of a human body as a clicked portion in a color selection area.

7 is a screen according to an embodiment of the present invention, a screen for diagnosing personal color by receiving a color of a human body by a numeric code.

8 is a screen according to an embodiment of the present invention, and is a screen for diagnosing personal color by receiving a color of a human body as a clicked part from an image photographed by a camera.

<Description of Symbols for Main Parts of Drawings>

10 ... color input 11 ... skin color input

12 ... color input 13 ... eye input

20 ... Operation Control Unit 30 ... DB

50 ... Display 100 ... Color input

101 ... skin color selection 102 ... hair color selection

103 ... Eye color selector 111, 121, 131 ... Color expression

110 Camera 120 Color Sensor

200 ... Diagnostic button 300 ... Small speed display

310 ... Season type indicator 320 ... Recommended color indicator

330 color intensity indicator

Claims (32)

A DB 30 storing a model color in which a season type is predefined; A color input unit 10 which receives a color of the human body's vagina; An operation control unit 20 for selecting a seasonal type based on a distance between a query color input from the color input unit 10 and a model color stored in the DB 30; A display unit (50) for displaying the selected season type according to a control signal of the calculation control unit (20); Personal color diagnostic system comprising a. A DB 30 storing a model color in which a season type is predefined and a recommendation color corresponding to the season type; A color input unit 10 which receives a color of the human body's vagina; An operation control unit 20 for selecting a recommendation color based on a distance between the query color input from the color input unit 10 and the model color stored in the DB 30; A display unit (50) for displaying the recommendation color selected by the calculation control unit (20) by the control signal of the calculation control unit (20); Personal color diagnostic system comprising a. A DB 30 storing a model color in which a season type is predefined; A color input unit 10 which receives a color of the human body's vagina; An operation control unit 20 for calculating a degree of belonging for each season type based on a distance between a query color input from the color input unit 10 and a model color stored in the DB 30; A display unit (50) for displaying the calculated degree of belonging by the control signal of the calculation control unit (20); Personal color diagnostic system comprising a. The method according to any one of claims 1 to 3, The color input unit 10, Personal color diagnosis system, characterized in that it comprises a color selection unit (100) for receiving the color of the human body by clicking the mouse. The method according to claim 4, The color selection unit 100, Skin color selection unit 101 is displayed with the yellow base and the blue base as the horizontal axis and brightness is the vertical axis, hair color display unit 102 and the eye color display unit 103 are displayed with the brightness as the vertical axis. Personal color diagnostic system. The method according to any one of claims 1 to 3, The color input unit 10, Personal color diagnostic system, characterized in that it comprises a color selection unit (100) for receiving the color of the human body by the numeric code. The method according to claim 6, The color selection unit 100 is provided with a text input window, the text input window personal color, characterized in that it comprises at least one of the skin color input unit 11, hair color input unit 12 and the eye color input unit 13 Diagnostic system. The method according to any one of claims 1 to 3, The color input unit 10, Personal color diagnosis system comprising a color selection unit (100) for receiving the color of the human body quality by clicking the mouse on the displayed image of the human body. The method according to claim 8, The color selector 100 may include at least one of a skin color image button 141, a hair color image button 151, and an eye color image button 161 for loading an image of the human body. Diagnostic system. The method according to claim 9, The image of the human body comprises at least one of a skin image (14), a hair image (15) and a pupil image (16). The method according to claim 1, The calculation control unit 20, Calculate a distance between the color of the query and the model color, And a season type of a model color corresponding to a distance of a minimum value from the calculated distance. delete The method according to claim 2, The calculation control unit 20, Calculate a distance between the color of the query and the model color, And a recommendation color corresponding to a seasonal type of model color corresponding to a distance of the minimum value from the calculated distance. delete The method according to claim 3, The calculation control unit 20, Calculate a distance between the color of the query and the model color, Personality diagnostic system, characterized in that to calculate the degree of belonging by dividing the inverse of the minimum distance for each season in the calculated distance by the total sum of the inverse of the minimum distance for each season, and to display the calculated degree of belonging. delete In a method for diagnosing personal color based on a model color in which a season type is predefined, Receiving the color of the vagina of the human body; Selecting a season type based on a distance between the input query color and the model color; Displaying the selected season type; Personal color diagnostic method comprising a. In a method for diagnosing personal color based on a model color in which a season type is predefined, Receiving the color of the vagina of the human body; Calculating a degree of belonging for each season type based on the distance between the received query color and the model color; Displaying the calculated degree of belonging; Personal color diagnostic method comprising a. The method according to claim 17 or 18, The model color is Personal color diagnosis method characterized in that it is constructed by surveying the color of the human body of the subject and the color of the season type preferred by the subject. The method according to claim 17 or 18, The model color is And a plurality of color analyzers are constructed by classifying seasonal types based on the human body color of the model using a plurality of ordinary people as models. The method according to claim 17 or 18, Receiving the color of the quality of the human body, Displaying the color selection unit 100 on the display unit 50; Receiving a color of a human body by clicking a mouse in the color selecting unit (100); Personal color diagnostic method comprising a. The method according to claim 17 or 18, Receiving the color of the quality of the human body, Displaying the color selection unit 100 on the display unit 50; Inputting a numeric code into the color selector (100); Receiving the input numeric code as a color of a human body's query; Personal color diagnostic method comprising a. The method according to claim 17 or 18, Receiving the color of the quality of the human body, Loading the photographed image; Displaying the loaded image on a display unit (50); Receiving a color of a human body by clicking a mouse on an image displayed on the display unit 50; Personal color diagnostic method comprising a. The method according to claim 23, Receiving the color of the human body by clicking the mouse on the image displayed on the display unit 50, Repeating at least two or more mouse clicks on the image displayed on the display unit (50); Calculating an average value of the colors clicked with the mouse; Receiving the calculated average value as a color of a human body's vagina; Personal color diagnostic method comprising a. The method according to claim 17 or 18, Receiving the color of the quality of the human body, Receiving the color measured by the color sensor 120 for measuring a color value as the color of the human body; Personal color diagnostic method comprising a. The method according to claim 25, Receiving the color measured by the color sensor 120 for measuring a color value as the color of the human body quality, Measuring at least two or more times with the color sensor 120 for measuring a color value; Calculating an average value of the measured color of the human body; Receiving the calculated average value as a color of the human vagina; Personal color diagnostic method comprising a. The method according to claim 17 or 18, The color of the vagina includes skin color, hair color and eye color. The method according to claim 17, Selecting a season type based on the distance between the received query color and the model color, Calculating a distance between the query color and a model color; Selecting a season type of the model color corresponding to the minimum value of the calculated distance as the season type of the color of the query; Personal color diagnostic method comprising a. delete 19. The method of claim 18, Computing the degree of belonging based on the distance between the input query color and the model color, Calculating a distance between the color of the query and the model color for each season; Selecting a distance having a minimum value for each season from the distance calculated for each season; Calculating a degree of belonging by dividing the inverse of the distance of the minimum value selected for each season by the sum of the inverses for the distance of the minimum value selected for each season; Personal color diagnostic method comprising a. delete A computer-readable recording medium having recorded thereon a program for executing the personal color diagnostic method according to any one of claims 17 to 18.

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