KR101881557B1 - Skin sebum estimating method using image - Google Patents

Abstract

A skin sebum degree measuring method using an image comprises the following steps of: allowing a computer device to detect a skin region of a subject in an image; allowing the computer device to extract color data and texture data of the skin region; and allowing the computer device to input at least one of the color data and the texture data to a previously arranged first regression analysis function by using a relationship of the at least one of the color data and the texture data and a sebum degree of a skin, and determine a first skin sebum degree. Therefore, the sebum degree of the skin can be easily measured by using the image captured by a camera or a smart device.

Description

{SKIN SEBUM ESTIMATING METHOD USING IMAGE}

The technique described below relates to a technique for measuring the degree of skin oil using an image.

BACKGROUND ART [0002] Recently, as interest in skin beauty has increased, devices for diagnosing a user's skin condition through cosmetic-care products have been attracting attention. Meanwhile, a service for managing the skin condition data by a smart phone is also provided.

Korean Patent Publication No. 10-2015-0033938

Conventional techniques for measuring or managing skin conditions basically require a separate device to measure skin condition. The technique described below is intended to provide a method of measuring the degree of sebum in skin using an image.

A method for measuring skin oiliness using an image includes the steps of: detecting a skin region of a subject in a video image by a computer device; extracting color data and texture data of the skin region of the computer device; And inputting at least one of the color data and the texture data to a first regression analysis function provided beforehand using a relationship between at least one of the texture data and the skin oiliness to determine a first skin oiliness.

In another aspect of the present invention, there is provided a method for measuring skin oil content using an image, comprising the steps of: detecting a skin region of a subject in an image; extracting color data and texture data of the skin region from the computer device; Estimating skin moisture by inputting at least one of the color data and the texture data to a first regression analysis function provided in advance using a relationship between at least one of the data and the texture data and the moisture content of the skin, And estimating skin oiliness by inputting the estimated skin moisture content to a second regression analysis function previously prepared using the relationship between the estimated skin moisture content and the skin oiliness.

The technique described below can easily measure the degree of oiliness of a skin using a camera or an image captured by a smart device.

FIG. 1 shows an example of a configuration of an apparatus for measuring skin oil fractions using images.
FIG. 2 is an example of a flowchart for a method of measuring skin oil content using an image.
FIG. 3 shows an example of a process of preparing a regression analysis function.
FIG. 4 is an example of a procedure for measuring skin oiliness according to the method of FIG.
FIG. 5 shows an example of a process of preparing an improved regression analysis function using the calculated skin oiliness.
6 is an example of a process for calculating improved skin oil fractions.
FIG. 7 is another example of a flowchart of a method for measuring skin oiliness using an image.
FIG. 8 shows another example of a process of preparing the regression analysis function of FIG.
FIG. 9 is an example of a procedure for measuring skin oiliness according to the method of FIG.
10 is another example of a procedure for measuring skin oiliness according to the method of FIG.

The following description is intended to illustrate and describe specific embodiments in the drawings, since various changes may be made and the embodiments may have various embodiments. However, it should be understood that the following description does not limit the specific embodiments, but includes all changes, equivalents, and alternatives falling within the spirit and scope of the following description.

The terms first, second, A, B, etc., may be used to describe various components, but the components are not limited by the terms, but may be used to distinguish one component from another . For example, without departing from the scope of the following description, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component. And / or < / RTI > includes any combination of a plurality of related listed items or any of a plurality of related listed items.

As used herein, the singular " include "should be understood to include a plurality of representations unless the context clearly dictates otherwise, and the terms" comprises & , Parts or combinations thereof, and does not preclude the presence or addition of one or more other features, integers, steps, components, components, or combinations thereof.

Before describing the drawings in detail, it is to be clarified that the division of constituent parts in this specification is merely a division by main functions of each constituent part. That is, two or more constituent parts to be described below may be combined into one constituent part, or one constituent part may be divided into two or more functions according to functions that are more subdivided. In addition, each of the constituent units described below may additionally perform some or all of the functions of other constituent units in addition to the main functions of the constituent units themselves, and that some of the main functions, And may be carried out in a dedicated manner.

Also, in performing a method or an operation method, each of the processes constituting the method may take place differently from the stated order unless clearly specified in the context. That is, each process may occur in the same order as described, may be performed substantially concurrently, or may be performed in the opposite order.

FIG. 1 shows an example of a configuration of an apparatus for measuring skin oil fractions using images. FIG. 1 illustrates an example of detecting a face in an image including a user's face. Skin oil content refers to the amount of oil contained in the skin.

FIG. 1 (a) is an example of measuring skin oiliness using a smart device 50 such as a smart phone. The user takes a face with the camera built in the smart device 50. The smart device 50 detects the face area from the source image and extracts the color data and texture data of the preset area. The smart device 50 measures skin oiliness based on at least one of color data and texture data.

1 (a), the smart device 50 includes a camera 51, a storage device 52, a computing device 53, and an output device 54. The camera 51 acquires the source image. The arithmetic unit 53 extracts color data and texture data for a skin region included in the source image, and performs an operation for measuring skin oil fractions from at least one of the color data and the texture data. The storage device 52 may temporarily store the source image. The storage device 52 may also store a regression analysis function used to measure skin oil fractions. The output device 54 can output the measured skin oil level.

FIG. 1 (b) is an example of measuring skin oiliness using a device such as a PC. The user shoots the face with the camera 81 connected to the computer 85. The computer 85 detects the face area from the source image and extracts the color data and texture data of the predetermined area. The computer 85 measures skin oiliness based on at least one of color data and texture data.

1 (c) shows an example in which the server 95 at a remote location measures skin oiliness using an image acquired by the user terminal 91. FIG. The user takes a face with a camera built in the user terminal 91. The user terminal 91 transfers the photographed source image to the server 95 via the network. In this case, the user terminal 91 includes a communication module for data transmission. The server 95 detects the face area in the source image and extracts the color data and texture data of the predetermined area set in advance. The server 95 measures skin oiliness based on at least one of color data and texture data. The server 95 may transmit the measured skin oil level to the user terminal 91.

In some cases, the user terminal 91 may detect the skin region from the source image and transmit it to the server 95. In this case, the server 95 extracts color data and texture data for the skin region, and measures skin oiliness using at least one of the color data and the texture data. Alternatively, the user terminal 91 may detect the skin region in the source region and extract color and texture data for the skin region. The user terminal 91 may transmit color and texture data for the skin region to the server 95. The server 95 may measure skin oiliness using at least one of color data and texture data.

2 is an example of a flowchart for a method 100 for measuring skin oil content using an image.

First, the camera acquires the source image (110). The source image includes a skin area of the user. The computer device detects the skin region in the source image captured by the camera (120). The algorithm by which the computer device detects the skin area can utilize various techniques known in the art.

The computer device extracts an average value of color data and texture data for the skin region (130). The computer device can extract color and texture data for the entire extracted skin area. The computer device may also extract color and texture data for a specific skin region from the acquired image. The computer device may continue to extract color data in successive images (consecutive frames).

Various values can be used for the color data. For example, (1) the color data may use at least one of an R value, a G value, and a B value based on the RGB color scheme. The color data may use an average value for at least one of R value, G value, and B value. (2) The computer device can convert the RGB color scheme to a different color scheme. For example, computer politics can convert the RGB color scheme to various color schemes such as YUV, HSV, YCbCr, YCgCo, and the like. The computer device can extract color data of at least one of chrominance components or luminance components of a specific color system. In this case, the color data may use one of the components that are less influenced by the surrounding environment (illumination, etc.). For example, in the case of YCbCr, at least one of Cb and Cr can be used. In the case of YCgCo, at least one of Cg and Co may be used. Further, one of the two chrominance components, which is more robust to the variation of the illuminance, can be used. For example, in the case of YCgCo, only Cg can be used. In this case, the computer device can extract the average value of the Cg color data of the skin region as color data. (3) Further, the color data may be a value obtained by combining weights applied to at least one color component in various color systems such as RGB, YUV, HSV, YCbCr, YCgCo, and the like. In the case of combining color components, the color data may be a value obtained by adding different weighted values depending on the color system and the type of color component.

Various kinds of texture data are used in image. Various measures such as contrast, angular second moment (ASM), entropy, dissimilarity, homogeneity, and direction are studied for the texture of the image.

The computer device applies the extracted color data and / or texture data to a regression analysis function provided in advance to calculate the degree of skin oil (140). The computer device may calculate at least one of the color data and the texture data to a regression analysis function to calculate an oil content of the skin.

Regression analysis is an analytical method that measures fit between two variables for continuous variables. Regression analysis calculates a function (formula) that calculates a specific value using predetermined sample data in advance. The regression function is expressed as an equation representing a regression line or a regression curve. The computer device extracts the color data and the texture data using the image, and at the same time, measures the skin oiliness for the same user using the skin oil measuring device. This process is repeated to generate a plurality of data, and the computer apparatus generates a regression analysis function that calculates skin oil fractions from the color data and the texture data based on the plurality of generated data.

FIG. 3 shows an example of a process of preparing a regression analysis function. The process of preparing samples for color data and texture data will be described. The user shoots a face image of a person using a smart device or a general camera (a process). The computer apparatus detects a face region displayed in a square in the source image including the face image (step b). The computer device detects the skin area in the face area (step c). The computer device extracts the average value of the color data and the texture data in the skin area (step d). In the extracted color data and texture data, the average value of the color and texture data is stored in DB. Fig. 3 shows an example of detecting a part of the face area A as a sample. Unlike FIG. 3, the entire face region may be detected to extract color data and texture data. A computer device typically provides a plurality of samples for color and texture data.

A sample for the color data and the texture data is prepared, and at the same time, an oil fraction is measured for the same user (e process). Fig. 3 shows an example of measuring the degree of fractions by a moisture meter at region B corresponding to region A. An oil meter is a commercially available meter. The skin oil content measured by the oil meter is stored in the skin oil database. When the computer device extracts the first color data and texture data, it simultaneously measures the first oil fraction with the oil meter. The computer device identifies the color data / texture data and the oil fractions measured by the oil meter so that they match each other. For example, the computer device may store the color data / texture data generated at the same time point and the oil level measured by the oil level meter in the same table or in the same order in different tables.

The computer apparatus generates a regression analysis function (step f) by using the skin oil fraction measured at the same time as any one of the color data and the texture data, and stores the generated regression analysis function in the regression analysis DB. The computer device can generate a regression analysis function using a plurality of sample data. As described above, the regression analysis function means a regression line or a regression curve.

The computer device can derive a regression linear equation using the color and texture data DB and the skin oil DB for the same area. The regression linear equation is shown in Equation 1 below.

Where x is the value of the color or texture data in the specified color scheme. a and b are constants obtained by regression analysis. y represents the calculated skin oil value. The results of applying the actual data may vary with the values of the constants a and b depending on the data used.

The regression curve is a curve obtained by representing a set of points on a correlation chart as a curve rather than a straight line. Regression curves show the relationship between two variables. The computer device can derive a regression curve equation using the color and texture data DB and the skin oil DB. The regression curve equation is shown in Equation 2 below.

Where x is the value of the color or texture data in the specified color scheme. a, b, and c are constants obtained by regression analysis. y represents the calculated skin moisture value. The result of applying the actual data may vary in constant a, b or c depending on the data used.

As described above, the computer device may use at least one of color data and texture data. A regression analysis function should be provided according to the input data. For example, a regression analysis function may be needed for (1) a regression analysis function for color data, (2) a regression analysis function for texture data, and (3) a combination of color and texture data. Furthermore, when color data and texture data are used, a regression analysis function may be prepared by assigning different weights to each data. (4) or a first regression analysis function for the color data and a second regression analysis function for the texture data are separately generated, and the first value and the texture data, to which the color data is applied to the first regression analysis function, The final skin oil fractions can be determined by assigning different weights to the second values applied to the skin.

On the other hand, the computer device may generate a regression analysis function considering various variables. For example, it is possible to generate different regression analysis functions depending on the type of skin area (forehead, ball, jaw, etc.), hardware specifications of the camera, In this case, the user would have to go through a process of entering variables into the computer device.

FIG. 4 is an example of a procedure for measuring skin oiliness according to the method of FIG.

The user shoots a face image of a person using a smart device or a general camera (a process). The computer apparatus detects a face region displayed in a square in the source image including the face image (step b). The computer device detects the skin area in the face area (step c). Fig. 4 shows an example in which the computer device detects a specific skin area A set in advance in the face area. Figure 4 illustrates an example in which a computer device detects a " ball " area.

If your computer device uses a color scheme other than RGB, you must perform a color scheme conversion. For example, if you use the YCgCo color scheme, the computer device converts the RGB color scheme of the skin region to the YCgCo color scheme.

The computer device extracts the average value of the color data and the texture data based on the skin area A (step d). For example, the computer device can extract color data from the average value of Co chrominance components in the YCgCo color system. Further, the computer device can extract the contrast value of the skin area A as texture data. The contrast value can be expressed as a flat (average) brightness value of the corresponding region. The match value is an example of texture data.

The computer device applies color data and / or texture data to a regression analysis function (e process). The regression analysis DB is prepared in advance through the process described in FIG. The computer device determines the result of the regression analysis function as the final skin oil (step f).

The computer device can calculate a single result using the weighted average of the color data and the texture data in the process of calculating skin oil fractions. Further, the computer apparatus can determine the degree of oil fractions as a result of weighting the oil fractions calculated using the individual data. For example, the computer device can determine the degree of oiliness as a result of weighting a skin oil fraction of 43.25 estimated through the color R and a skin oil fraction of 43.57 estimated through a homogeneity to a weight of 5: 5.

Further, the computer apparatus may use a multiple regression analysis equation. Equation 3 below is an example of a multiple regression analysis equation.

In Equation (3), y represents the skin oil fraction estimated as a dependent variable. β is a constant calculated through multiple regression analysis. β ₁ to β _n are coefficients of x, and x is color or texture data as an independent variable. By using various color and / or texture data at the same time through this method, improved error rates can be obtained for various environmental changes.

FIG. 5 shows an example of a process of preparing an improved regression analysis function using the calculated skin oiliness. The user shoots a face image of a person using a smart device or a general camera (a process). The computer apparatus detects a face region displayed in a square in the source image including the face image (step b). The computer device detects the skin area in the face area (step c). Fig. 5 shows an example in which the computer device detects a specific skin area A set in advance in the face area. Figure 5 shows an example in which a computer device detects a " ball " region. The computer device extracts the average value of the color data and the texture data based on the skin area A (step d). The computer device applies color data and / or texture data to a regression analysis function (e process). The regression analysis DB used in the e-process is prepared through the process described in Fig. The computer device calculates skin oil fractions in a regression analysis function (step f). The developed regression analysis function is created by using the calculated skin oil and skin oil database measured by the oil meter (g process), and the generated regression analysis function is stored in the improved regression analysis DB. As described above, the regression analysis function means a regression line or a regression curve.

6 is an example of a process for calculating improved skin oil fractions. The user shoots a face image of a person using a smart device or a general camera (a process). The computer apparatus detects a face region displayed in a square in the source image including the face image (step b). The computer device detects the skin area in the face area (step c). Fig. 6 shows an example in which the computer apparatus detects a specific skin area A preset in the face area. Figure 5 shows an example in which a computer device detects a " ball " region. The computer device extracts the average value of the color data and the texture data based on the skin area A (step d). The computer device applies color data and / or texture data to a regression analysis function (e process). The regression analysis DB used in the e-process is prepared through the process described in Fig. The computer device calculates skin oil fractions in a regression analysis function (step f).

The computer predicted skin oil fraction is applied to an improved regression analysis function (g process). The improved regression analysis DB used in the process g is prepared in the process of FIG. The computer device determines an ultimate skin oil fraction by calculating an improved skin oil fraction in an improved regression analysis function (h process).

The computer device can calculate a single result using the weighted average of the color data and the texture data in the process of calculating skin oil fractions.

The computer device can derive regression line equation and regression curve equation using the calculated skin oil content and skin oil database. The regression linear equation is as shown in Equation 1, where x is the value of the calculated skin oil content. a and b are constants obtained by regression analysis. y represents an improved skin oil value. The results of applying the actual data may vary with the values of the constants a and b depending on the data used.

Regression curves can also be used to derive a regression curve equation using the calculated skin oil and skin oil database. The regression curve equation is as shown in Equation 2, where x is the value of the calculated skin oiliness. a, b, and c are constants obtained by regression analysis. y represents an improved skin oil value. The result of applying the actual data may vary in constant a, b or c depending on the data used.

Figure 7 is another example of a flowchart for a method 200 for measuring skin oil fractions using images. First, the camera acquires the source image (210). The source image includes a skin area of the user. The computer device detects the skin region from the source image captured by the camera (220). The algorithm by which the computer device detects the skin area can utilize various techniques known in the art.

The computer device extracts an average value of color data and texture data for the skin region (230). The computer device can extract color and texture data for the entire extracted skin area. The computer device may also extract color and texture data for a specific skin region from the acquired image. The computer device may continue to extract color data in successive images (consecutive frames). As described above, various values of color data can be used. For example, (1) the color data may use at least one of an R value, a G value, and a B value based on the RGB color scheme. The color data may use an average value for at least one of R value, G value, and B value. (2) The computer device can convert the RGB color scheme to a different color scheme. For example, computer politics can convert the RGB color scheme to various color schemes such as YUV, HSV, YCbCr, YCgCo, and the like. The computer device can extract color data of at least one of chrominance components or luminance components of a specific color system. In this case, the color data may use one of the components that are less influenced by the surrounding environment (illumination, etc.). For example, in the case of YCbCr, at least one of Cb and Cr can be used. In the case of YCgCo, at least one of Cg and Co may be used. Further, one of the two chrominance components, which is more robust to the variation of the illuminance, can be used. For example, in the case of YCgCo, only Cg can be used. In this case, the computer device can extract the average value of the Cg color data of the skin region as color data. (3) Further, the color data may be a value obtained by combining weights applied to at least one color component in various color systems such as RGB, YUV, HSV, YCbCr, YCgCo, and the like. In the case of combining color components, the color data may be a value obtained by adding different weighted values depending on the color system and the type of color component.

Various kinds of texture data are used in image. Various measures such as contrast, angular second moment (ASM), entropy, dissimilarity, homogeneity, and direction are studied for the texture of the image.

The computer device applies the extracted color data and / or texture data to a first regression analysis function provided in advance to calculate the moisture of the skin (240). The regression analysis function used in this process is prepared by using the relationship between the color data and / or the texture data in the same region as the moisture content of the skin region previously measured by the moisture meter.

The computer device calculates the skin oiliness by applying the newly obtained skin oiliness to the second regression analysis function (250). That is, the method of FIG. 7 estimates the water content using the correlation between skin moisture and oil content, and then estimates the oil content further.

FIG. 8 shows another example of a process of preparing a regression analysis function. For the subject, the oil content of the skin is measured with an oil meter (a process). In this case, the area where the oil is measured is assumed to be the 'ball' area (denoted by B). The skin oil DB stores the measured oil data. On the other hand, the moisture of the skin is measured by a moisture meter on the subject (step b). The area for measuring moisture is also a 'ball' area. The skin moisture DB stores the measured moisture data. The computer device analyzes the relationship between the water data and the water data measured at the same point in time and calculates a regression line and / or a regression curve (c process). The calculated regression equation is stored in an oil / water regression analysis DB.

FIG. 9 is an example of a procedure for measuring skin oiliness according to the method of FIG. The user shoots a face image of a person using a smart device or a general camera (a process). The computer apparatus detects a face region displayed in a square in the source image including the face image (step b). The computer device detects the skin area in the face area (step c). Fig. 9 shows an example in which the computer device detects a specific skin area A set in advance in the face area. Figure 5 shows an example in which a computer device detects a " ball " region. That is, in order to prepare the regression equation in FIG. 8, the same area as the area where the moisture data and the oil data are measured is used. The computer device extracts the average value of the color data and the texture data based on the skin area A (step d). The computer device applies color data and / or texture data to a regression analysis function (e process). The water regression analysis DB stores a regression equation prepared in advance according to the correlation between the color data and / or the texture data in the image and the actually measured water. The computer device calculates skin moisture from the regression analysis function (step f).

The computer predicted skin moisture content is applied to the regression equation prepared in FIG. 8 (g process). The oil / moisture regression analysis DB used in the process g is prepared in the process of FIG. The computer device calculates skin oil fractions in a regression analysis function based on the relationship between oil and moisture (step h).

10 is another example of a procedure for measuring skin oiliness according to the method of FIG. The user shoots a face image of a person using a smart device or a general camera (a process). The computer apparatus detects a face region displayed in a square in the source image including the face image (step b). The computer device detects the skin area in the face area (step c). Fig. 9 shows an example in which the computer device detects a specific skin area A set in advance in the face area. Figure 5 shows an example in which a computer device detects a " ball " region. That is, in order to prepare the regression equation in FIG. 8, the same area as the area where the moisture data and the oil data are measured is used. The computer device extracts the average value of the color data and the texture data based on the skin area A (step d). The computer device applies color data and / or texture data to a regression analysis function (e process). The water regression analysis DB stores a regression equation prepared in advance according to the correlation between the color data and / or the texture data in the image and the actually measured water. The computer device calculates skin oil fractions in a regression analysis function (step f).

The skin moisture, the color data and / or the texture data extracted from the image are applied to the multiple regression analysis equation stored in the computer multiple regression analysis DB (step g). For example, the multiple regression analysis equation may be as shown in Equation 4 below. Multiple regression analysis can be used to improve the error rate.

는 다중회귀분석을 통해 계산된 상수이다.

,

및

는 각각 수분, 색상, 질감의 계수이다. 상기 방법을 통해 다양한 수분, 색상, 질감 데이터를 동시에 사용함으로써 환경 변화에 더 개선된 오차율을 얻을 수 있다.y is a dependent variable and represents the skin oil fraction calculated by the equation above.

Is a constant calculated through multiple regression analysis.

,

And

Are coefficients of moisture, color, and texture, respectively. By using various moisture, color, and texture data at the same time, it is possible to obtain a more improved error rate in the environmental change.

The computer device calculates skin oiliness according to the result of the multiple regression analysis (step h).

The present embodiment and drawings attached hereto are only a part of the technical idea included in the above-described technology, and it is easy for a person skilled in the art to easily understand the technical idea included in the description of the above- It will be appreciated that variations that may be deduced and specific embodiments are included within the scope of the foregoing description.

50: Smart devices
51: Camera
52: Storage device
53:
54: Output device
81: Camera
85: Computer
91: User terminal
95: Server

Claims (11)

Detecting a region of the subject's skin from the image;
The computer device extracting color data and texture data of the skin region; And
Wherein the computer device inputs at least one of the color data and the texture data to a first regression analysis function provided in advance using a relationship between at least one of the color data and the texture data and the skin oil content, Determining a degree of freedom,
Wherein the first skin oil level is a value obtained by assigning different weights to the texture-based skin oil level determined using at least one of the color-based skin oil fraction determined using at least one of the components of the color data and the components of the texture data A method for measuring skin oil content using images that are summed and determined. The method according to claim 1,
Wherein the color data is at least one of a color system of at least one of RGB, HSV, YUV, YCgCo, and YCbCr. The method according to claim 1,
Wherein the texture data is data on at least one of contrast, angular second moment, entropy, dissimilarity, and homogeneity. The method according to claim 1,
Wherein the computer device is configured to calculate the skin color of the subject by using at least one of the average value and the texture data of the skin region included in the source image of the subject and the skin oil level of the subject measured by the oil analyzer at the time of shooting the source image, A Method of Estimating Skin Fraction Using Image with Regression Analysis Function. 5. The method of claim 4,
Wherein the first skin oil distribution is applied to a second regression analysis function previously prepared using the skin oil level of the subject measured by the oil analyzer at the time when the computer device photographs the first skin oil and the source image, And determining a second skin oil content. delete Detecting a region of the subject's skin from the image;
The computer device extracting color data and texture data of the skin region;
Wherein the computer device inputs at least one of the color data and the texture data to a first regression analysis function provided in advance using a relationship between at least one of the color data and the texture data and the moisture content of the skin, step; And
And estimating skin oiliness by inputting the estimated skin moisture content to a second regression analysis function previously prepared using the relationship between the estimated skin moisture content and the skin oil content, Method for measuring skin oil content. 8. The method of claim 7,
Wherein the color data is at least one component of at least one color scheme of RGB, HSV, YUV, YCgCo, and YCbCr, and the texture data includes at least one of contrast, dissimilarity and homogeneity A method for measuring skin oiliness using image data. 8. The method of claim 7,
Wherein the computer device is configured to calculate the skin color of the subject by using at least one of the average value and the texture data of the skin region included in the source image of the subject and the skin oil level of the subject measured by the oil analyzer at the time of shooting the source image, A Method for Measuring Skin Oil Perception Using an Image Providing a Regression Analysis Function. 10. The method of claim 9,
Wherein the computer apparatus further comprises a skin oil level measurement method using an image to provide the second regression analysis function using the estimated skin moisture level and the skin oil level of the subject measured by an oil level meter at the time of shooting the source image . 8. The method of claim 7,
Wherein the second regression analysis function is a multiple regression analysis function having at least one of the color data and the texture data and the estimated skin moisture distribution as a variable.

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