WO2008001558A1 - Method of estimating evaluation data on the skin smoothness with naked eye and estimation apparatus - Google Patents

Abstract

It is intended to provide a method of estimating the evaluation data on the skin smoothness with the naked eye, an apparatus for estimating the evaluation data on the skin smoothness with the naked eye and a program therefor so that the evaluation data on the skin smoothness with the naked eye can be conveniently, objectively and quantitatively estimated by anyone, Based on the irregularities on the skin surface of a subject, a roughness parameter and a fractal dimension are calculated. Then, the roughness parameter, fractal dimension and age of the subject are assigned to a multiple regression formula, which has been preliminarily prepared to indicate the relationship between the roughness parameter, the fractal dimension and the age and the evaluation data on the skin smoothness with the naked eye, to thereby estimate the evaluation data on the skin smoothness with the naked eye.

Description

 Specification

 Method and apparatus for estimating visual evaluation value of smoothness of skin

 Technical field

 [0001] The present invention relates to a method and an apparatus for estimating a visual evaluation value of the smoothness of a skin, and more specifically, easily estimates a visual evaluation value of a smoothness of a skin at a store selling cosmetics. The present invention relates to a method, and an apparatus and program used for the method.

 Background art

 [0002] Recognizing beauty and skin by a third party is one of the great wishes of many people as well as women. For this reason, research and development of cosmetics and beauty methods to make the skin look beautiful is being actively conducted. However, since the skin condition varies greatly depending on the individual, and also changes depending on the aging and living environment, the type of cosmetics, makeup method, skin care method, etc. are selected appropriately. In order to do this, it is necessary to objectively determine how the target skin looks to a third party. For example, at department stores' cosmetics stores, pharmacies, and cosmetics stores, there is a need for a simple method that objectively evaluates the degree of skin beauty of the subject.

 The skin beauty of a third party can be broadly divided into “skin trouble! /,” A negative factor and a positive factor “skin smoothness”. It is made clear from the results of various surveys.

 [0003] "Smoothness of the skin" is formed by a combination of various factors such as fineness of texture, uniformity of texture direction, unevenness and smoothness. Therefore, in order to accurately estimate the smoothness of the skin by the third party's visual observation, that is, the “visual evaluation value of the smoothness of the skin”, the form and moisture content of the subject's skin surface are measured, It has been necessary to analyze the results of these measurements and to have a skin assessment specialist observe the subject's skin and perform a visual functional assessment based on his specialized knowledge. However, these methods are difficult to say as simple evaluation methods because they require a large amount of data collection, complex data analysis, and training and training of skin evaluation specialists.

On the other hand, techniques for measuring the shape of the skin surface and evaluating the properties of the skin surface have been developed. For example, a method of measuring the three-dimensional shape of a replica of the skin surface and converting the measurement data into a pitch signal to evaluate the state of the skin surface morphology (Patent Document 1), using a parameter representing the skin surface roughness as an index And a method for detecting morphological characteristics of the skin surface (Patent Document 2) and a method for evaluating the skin surface condition by analyzing unevenness data of the skin surface (see Patent Document 3). Yes.

 In addition, techniques have been developed to measure skin surface properties by various methods and to easily evaluate the skin condition using the processed values of the measured values. For example, a method for quantitatively evaluating the skin surface shape and optical properties by processing image information obtained by imaging the skin surface and its replica with an appropriate photoelectric conversion means by a program has been reported. For example, by applying multiple light sources to the skin replica and extracting the skin groove pattern, the device can analyze the skin gap spacing and skin direction (see Patent Document 4) and the brightness of the skin surface image in multiple stages. A method for measuring the skin surface shape by converting the digital signal value into a digital signal value and statistically processing the digital signal value is disclosed (see Patent Document 5).

 By such a method, it has become possible to collect skin surface morphology information at high speed and with high accuracy, and to easily evaluate the skin based on this conventional method. However, the factors that form the smoothness of the skin visually by third parties are unknown, and these factors are considered to be related in a complex manner. It is still difficult to accurately estimate the visual evaluation value.

 Under such circumstances, it is necessary to find an important factor that forms the smoothness of the skin visually by a third party, and to develop a technique for estimating the visual evaluation value of the smoothness of the skin at a higher speed and with higher accuracy. It has been.

On the other hand, the concept of fractal is a geometric concept used for self-similar figures created in mathematics research. Also, it is known that there are many things in nature that have a fractal shape! It is known to obtain the fractal dimension as one means of expressing the shape of the fractal nature. In recent years, a method of determining a specific state in a living body by calculating a fractal dimension has been reported. For example, the biological signal of the subject's characteristic anxiety level is analyzed by fractal analysis, and the anxiety level is evaluated from correlation with statistical data (Patent Document 6). A method for investigating a tissue state by fractal analysis of a sound pulse signal (Patent Document 7) and an automatic detection system for malignant cells using a fractal analysis (Patent Document 8) have been disclosed. Further, a method for evaluating the distribution of skin melanin pigments from the correlation between the pigment distribution of melanin and the like and the fractal dimension of the luminance of the pixels constituting the skin image has been disclosed (Patent Document 9). It has also been reported that skin age may be estimated using the fractal dimension of skin property values as an index (Non-patent Document 1). However, the specific method of using the fractal dimension has not been clarified, and the relationship between the fractal dimension and the skin age, the relationship between the fractal dimension and the visual evaluation value of the three-dimensional shape of the skin surface, etc. has been found. Not.

 [0005] Patent Document 1 Japanese Patent Application Laid-Open No. 05-146412

 Patent Document 2 Japanese Patent Laid-Open No. 05-329329

 Patent Document 3 JP 04-305113 A

 Patent Document 4 Japanese Unexamined Patent Publication No. 61-64232

 Patent Document 5 Japanese Patent Laid-Open No. 02-46833

 Patent Document 6 JP 2001-299702 A

 Patent Document 7 Special Table of Japanese Patent Publication 11--507846

 Patent Literature 8 Special Table 2001--512824

 Patent Document 9 JP 2000-135207 A

 Non-patent document 1: “Skin age estimation method using skin image features and application to prevention of skin aging” (Masao Kasuga, New Technology Briefing Meeting from Northern University of Northern Tokyo, December 2, 2005) Document)

 Disclosure of the invention

[0006] Under such circumstances, the present inventors have analyzed the relationship between various property values representing the form and properties of the skin and visual evaluation values such as the beauty of the skin and the three-dimensional shape of the skin. I came. Based on these results, we have invented a method for estimating visual evaluation values such as the beauty of the skin and the three-dimensional shape of the skin simply, objectively and quantitatively, and have already filed patent applications. For example, using a roughness parameter such as Ra for skin replicas to objectively evaluate visible skin beauty (Japanese Patent Application No. 2005-327355), the color system of skin surface images image A method for estimating the visual evaluation value of the three-dimensional shape of the skin surface from the fractal dimension of the signal distribution or the distribution of the undulation value of the skin (Japanese Patent Application No. 2006-046654), the color system of the image of the skin surface This is a method for estimating the visual evaluation value of the beauty of skin from the fractal dimension of the distribution of image signals or the fractal dimension of the distribution of skin relief (Japanese Patent Application No. 2006-046659).

 It is an object of the present invention to provide a further means for providing useful information for discrimination of skin condition and appropriate selection of a cosmetic. More specifically, the present invention provides a method for estimating the visual evaluation value of the smoothness of the skin, an apparatus and a program for estimating the visual evaluation value of the smoothness of the skin, and the visual evaluation of the smoothness of the skin. The task is to enable anyone to easily and objectively and quantitatively estimate values. In addition, the present invention finds a skin property value largely related to a visual evaluation value of skin smoothness, a processed value thereof, and a combination of these values, and obtains a visual evaluation value of skin smoothness accurately and easily. The challenge is to provide a method.

 In order to solve the above problems, the present inventors seek a means for accurately estimating the visual evaluation value of the smoothness of the skin, and combine various skin property values, processed values, and these numerical values. In particular, we have been examining it.

 In the process, the present inventors paid attention to the relationship between the parameter representing the surface properties of the skin surface and the fractal dimension of the distribution of undulation values and the actual evaluation of the skin by a third party. Analyze how the fractal dimension of the cross-sectional curve parameter, roughness parameter, waviness parameter, and skin property values that represent the surface properties of the skin surface is related to the visual evaluation of the skin surface. It has been repeated. As a result, it was found that the roughness parameter and the fractal dimension calculated from the undulation value on the skin surface are values indicating important factors that form the smoothness of the skin. They found that there is a relationship between these values and age, and the results of evaluating the smoothness of the skin by a third party. And by using this relationship, it was found that the visual evaluation value of the smoothness of the skin can be accurately estimated using the roughness parameter and fractal dimension of the skin surface of the subject, and the present invention has been completed.

That is, the present invention is as follows. (1) a step of acquiring the undulation value of the subject's skin surface;

 A step of calculating a roughness parameter of the undulation force force skin surface;

 Calculating the fractal force force fractal dimension of the skin surface;

 In the multiple regression equation showing the relationship between the skin surface roughness parameter, the skin surface fractal dimension and age, and the visual evaluation value of the smoothness of the skin, the calculated roughness parameter and fractal dimension of the subject, And assigning age to obtain visual evaluation value of smoothness of skin

 A method for estimating a visual evaluation value of the smoothness of the skin.

 (2) The method according to (1), wherein the roughness parameter force is at least one of Rmr, RSm, and Ra.

 (3) The method according to (1) or (2), wherein the fractal dimension is calculated by a box counting method.

 (4) Determining power of box size in the box counting method The method according to (3), characterized in that it is performed based on the standard deviation of the undulation value in the box.

 (5) The method according to any one of (1) to (4), wherein the undulation value also calculates skin replica force.

 (6) Estimate the visual evaluation value of the smoothness of the skin before and after the use of the external preparation for skin by the method for estimating the visual evaluation value of the smoothness of the skin as described in any one of (1) to (5). A method for evaluating an external preparation for skin, characterized in that the external preparation for skin is evaluated using the result of comparing the visual evaluation values of as an index.

 (7) means for inputting the age of the subject;

 Means for obtaining the undulation value of the skin surface of the subject;

 Means for calculating a roughness parameter of the undulating force force,

 Means for calculating the fractal dimension of the skin surface from the undulation value;

In the multiple regression equation showing the relationship between the skin surface roughness parameter, the skin surface fractal dimension and age, and the visual evaluation value of the smoothness of the skin, the calculated roughness parameter and fractal dimension of the subject, And a means for substituting the age and obtaining a visual evaluation value of the smoothness of the skin, Means for displaying the obtained visual evaluation value

 A visual evaluation value estimation device for the smoothness of the skin.

 (8) computer

 Means for calculating the skin surface roughness parameter from the undulation value of the subject's skin surface, means for calculating the fractal dimension of the skin surface from the undulation value of the subject's skin surface, a prepared skin surface roughness parameter, skin By substituting the calculated roughness parameter, fractal dimension, and age of the subject into the multiple regression equation showing the relationship between the surface fractal dimension and age and the visual evaluation value of the smoothness of the skin, the smoothness power of the skin Means for obtaining visual evaluation values

 Program for visual evaluation value of smoothness of skin for functioning as Brief Description of Drawings

FIG. 1 is a diagram showing an example of a measurement target region of a face part.

 FIG. 2 is a diagram showing undulation value data obtained from a replica corrected by a Sine function.

 FIG. 3 is a diagram showing the concept of division in the box counting method.

 FIG. 4 is a diagram showing the concept of counting in the box counting method.

 FIG. 5 is a diagram showing a fractal dimension.

 FIG. 6 is a hardware block diagram of an estimation apparatus a that is an example of the estimation apparatus of the present invention.

 FIG. 7 is a diagram illustrating the concept of undulation value filtering.

 FIG. 8 is a diagram showing an example of a skin image used for visual evaluation (photo).

 FIG. 9 is a view showing a tin plate shape in the aging process.

 FIG. 10 is a diagram showing a relationship (scatter diagram) between the visual evaluation value of the smoothness of the skin estimated using Equation (7) and the visual evaluation value of the actual smoothness of the skin.

 FIG. 11 is a diagram showing the relationship (scatter plot) between the visual evaluation value of the smoothness of the skin estimated using equation (9) and the visual evaluation value of the actual smoothness of the skin.

 BEST MODE FOR CARRYING OUT THE INVENTION

[0010] The method for estimating the visual evaluation value of the smoothness of the skin of the present invention (hereinafter, also referred to as "estimation method of the present invention") includes the following steps.

(A) The process of acquiring the undulation value of the skin surface of the subject (B) Step for calculating the roughness value force skin surface roughness parameter

 (C) Step of calculating the fractal dimension of the undulating force force on the skin surface

 (D) In the multiple regression equation showing the relationship between the skin surface roughness parameter, the skin surface fractal dimension and age, and the visual evaluation value of the smoothness of the skin prepared in advance, the subject's calculated roughness parameter, fractal Substituting dimensions and age to obtain a visual evaluation value of the smoothness of the skin

 [0011] In the estimation method of the present invention, the "visual evaluation value of the smoothness of the skin" is a statistical evaluation value that represents how smooth the skin looks when a third party looks at the skin. Yes, specifically, it is a statistical evaluation value obtained by repeatedly determining which skin looks smooth when comparing one skin with another. That is, in the estimation method of the present invention, the “visual evaluation value of the smoothness of the skin” is not a subjective evaluation value obtained by processing and processing information obtained by visual observation using human spiritual activity.

 [0012] (A) A step of acquiring the undulation value of the skin surface of the subject

 The undulation value is a numerical value indicating how high the reference surface force is at a point covering the surface of an object. The method of measuring the undulation value on the skin surface can be performed by a commonly used method, and may be measured directly from the skin or may be made by measuring a replica of the skin. It is preferable to measure the viewpoint power to be obtained by making a replica of the skin.

[0013] The replica agent used for producing the skin replica can be used without particular limitation as long as it is usually used for skin diagnosis or the like. For example, a replica agent using a silicon or polyvinyl alcohol (PVA) film is preferably shown. An example of such a replica agent is Asahi Biomed's Silicon ASB-01-WW, etc. In addition, the skin part for which a replica of the skin surface of the subject is acquired is not particularly limited as long as it is a part for which a visual evaluation value of the smoothness of the skin is to be estimated. Part. When the visual evaluation value of the smoothness of the skin is used for selection of cosmetics, it is preferable to obtain a replica of wrinkles. For example, if you set a measurement area of 2cm * 2cm (see Figure 1) on the buttock and get a replica of the part that contains it. The replica can be obtained by a conventional method used for diagnosing skin shape and the like. For example, after washing your face, apply it to your skin for 20 minutes at 20 ° C and 50% humidity. Apply a power agent and collect it.

 [0014] The method for obtaining the undulation value from the replica is not particularly limited, and a normal method can be used. For example, “Guide Evaluation Method Guidance”, Journal of the Japan Cosmetic Science Association, Vol. 28, No. 2 (2004) can be referred to.

 Specifically, for example, using a commercially available laser-type 3D surface roughness meter, the horizontal force (X direction) and vertical direction ( and a method of measuring by performing a laser scan in the y direction). Examples of such 3D roughness meters include high-precision 3D image processing equipment LIP (for example, LIP-50) from Science Systems Inc., SURFCOM from Tokyo Seimitsu Co., Ltd., VLH, PRIMOS (GFM Derma-TOP-blue (Breuckmann). The scan interval when measuring the undulation value using these instruments is not particularly limited as long as sufficient data can be obtained to calculate the roughness parameter, but the interval is 10 / zm or less. It is preferable. For example, when scanning with LIP-50, perform 1000 scans at 10 m intervals in the x-direction and the Z- or y-direction vertical direction in the replica area where x * y is lcm * 1 cm. Can do.

 In addition, when obtaining the undulation value in this way, if the sampling periods in the X and y directions are different, it is preferable to correct the sampling period using the Sine function (see Fig. 2). This is because the fractal dimension described later can be accurately calculated by such correction.

 [0015] In addition, the replica is irradiated with oblique illumination using an optical projection device or the like, and the shadow portion of the replica convex portion is extracted, and the area, width, etc., the depth of the concave portion of the skin, the area ratio, etc. are measured. Depending on the method, undulation values can also be obtained. Obtaining the relief value by irradiation with such oblique illumination is preferable because it is simple. The undulation value can be obtained by this method using, for example, a reflective 3D replica analysis system (Asahi Biomed).

[0016] It is also possible to irradiate light to a translucent replica, obtain the thickness of the replica from the amount of transmitted light, and obtain the undulation value of the replica force (translucent replica light transmission method). For example, 3D skin analysis system ASA-03 (Asahi BioMed) can be used to acquire the relief values by this method. [0017] In addition, as a method for obtaining the relief value directly from the skin, for example, a method in which lattice-shaped light is applied to the skin and the characteristic of the light distortion is converted into the relief value, and a commercially available device is used. It can be carried out. For example, it is possible to use devices such as PRIMOS and derma-TOP-blue.

 [0018] (B) Calculation of roughness parameter

 The roughness parameter of the subject's skin surface is calculated from the undulation value of the skin surface obtained as described above.

 “Roughness parameter” refers to parameters for calculating the roughness curve force defined by JIS standards, and 3D extended versions of these parameters (3D surface texture parameters).

[0019] The meter that also calculates the roughness curve force defined in JIS standard is “Product geometric characteristics specification (GPS) —Surface property: Contour curve method—Terminology, definition and surface property parameter, JISB0601: 2001 J Ra (arithmetic mean length of roughness curve), RSm (average length of roughness curve), Rmr (load length ratio of roughness curve), Rp (maximum peak height of roughness curve) , Rv (Maximum depth of roughness curve), Rz (Maximum height of roughness curve), Rc (Average height of roughness curve), Rt (Maximum section height of roughness curve), Rq (Roughness) The root mean square height of the roughness curve, Rsk (the roughness curve skewness), Rku (the roughness curve kurtosis), etc. In the estimation method of the present invention, Ra, RSm and Rmr are used. It is particularly preferred to use Rmr because these roughness parameters are in any direction obtained from the area where the undulation values are measured. If Rmr (Rmr in the horizontal direction) is used as the roughness parameter, it is preferable to obtain it from the extracted roughness curve in the horizontal direction (see Fig. 1). Yes.

 Examples of the three-dimensional surface property parameter include Sa, Sp, Sv, Sz, Sq, Ssk, Sku, and Smr.

 [0020] Further, in the estimation method of the present invention, when the area of the analysis is as small as possible, or when the waviness of the analysis surface is ensured, the long wavelength of the cross-sectional curve is obtained. If the component is small, the section curve parameters such as Pa, PSm, and Pmr can be used as roughness parameters.

The calculation of the skin surface roughness parameter is performed as follows. First, a cross-sectional curve of the skin surface is obtained from the obtained undulation value, and this is filtered to separate into a waviness curve (long wavelength component) and a roughness curve (short wavelength component). Examples of filter processing include Gaussian filter processing and 2CR filter processing. In the estimation method of the present invention, it is preferable to use Gaussian filter processing. The cutoff value c of the filter is preferably 3.5 to 0.5 mm.

 The calculation of the roughness parameter from the roughness curve is performed by a method based on the JIS standard. For the parameters of Rmr, RSm, Ra, Rp, Rv, Rz, Rc, Rt, Rq, Rsk, Rku, see “Product Geometric Characteristics Specification (GPS) —Surface Properties: Contour Curve Method—Terminology, Definitions and Surface Properties” The calculation should be based on the parameter, JIS B 0601: 2001. For example, HYPERLINK "https://orion.nagaokaut.ac.jp/ 3Dpara / index.html https://onon.nagaoKaut.ac.jp/3Dpara/index.html In order to calculate these surface roughness parameters, for example, the software included with the 3D surface roughness meter, Asahi Biomed's replica analysis software ASA— 03 — R General purpose software such as TalyHobson's Talymap3D analysis software or custom software can be used.

[0022] (C) Calculation of fractal dimension

 The fractal dimension of the skin surface is calculated from the undulation value of the skin surface acquired above.

 Examples of methods for calculating the fractal dimension include a box counting method (box-counting), a correlation dimension method, and a fractional Brownian motion model.

[0023] In the box counting method, a square (cube) that completely covers an object is divided into squares (cubes) of any size, and a divided square that covers the size of the square (cube) and part of the object. This is a method for obtaining a fractal dimension from the relationship with the number of (cubes). When a certain shape is specifically a square (cube) that completely covers the object divided by the length h of one side, the number of squares (cubes) that cover part of the object is N (h), Between r and N (h), N (h) = c'h— ^D (c is a constant coefficient)…

If the above approximate expression holds with a good correlation, the object is a fractal shape. At this time, D in Eq. (1) is the fractal dimension. Therefore, to obtain the fractal dimension D by the box counting method, c · h and N (h) are logarithmically plotted. Find the slope of the straight line.

 [0024] Such a box counting method is very simple and can be processed at high speed by a computer. However, the farther the target fractal dimension is from the half-integer value, the lower the analysis accuracy. Therefore, the effective box size is determined based on the standard deviation of the data in the box rather than simply determining whether or not a part of the object enters the box as in the general box counting method, It is preferred to use a method that includes the step of determining whether a portion of the object falls within the box.

 [0025] The calculation of such a fractal dimension can be specifically performed by the following method.

 (1) First, as shown in Fig. 3, the two-dimensional discrete data f (x, y) existing in size XX Y is divided into areas S (X, y) of size hX h (m). The data in the present invention is data of the undulation value obtained above, that is, the height of the reference surface force. h can be arbitrarily determined.

 [0026] (2) For each of the regions S to S, the standard deviation σ σ of the undulation value is expressed by the following equation (2)

 1 m

 (See Fig. 4)

[0027] [Equation 1]

Λ: Number of data in region S (Λ χ /;)

 σ ,. = JJ: Data value at point j when raster scanning area s: Average of data in area s

• · · (2)

[0028] (3) N (h) at size h is calculated by the following equation (3).

[0029] [Equation 2]

[0030] By calculating N (h) in this way, the standard deviation in region 11 of 11 11 is valid. Since the number of boxes can be counted as a typical box height, it is possible to suppress the influence of sudden noise such as noise in data measurement. In addition, a wide scaling range of 1 to 2 digits, which is indispensable for fractal dimension estimation, is obtained.

(4) The size h is increased and f (x, y) is subdivided, and N (h) is calculated in the same manner using the procedures (1) to (3).

 (5) Repeat (4) until h = X or h = Y, and calculate N (h).

 (6) Obtain the gradient force D of the graph representing the relationship between logN (h) and logh (see Fig. 5).

 [D] Estimation of visual evaluation value of smoothness of skin using multiple regression equation

 (D—1) Creating multiple regression equations

 Estimate the visual evaluation of the smoothness of the subject's skin using the roughness parameter of the subject's skin obtained in (b) above, the fractal dimension of the subject's skin obtained in (c), and the age of the subject. To do. A multiple regression equation showing the relationship between the skin surface roughness parameter, the fractal dimension and age of the skin surface, and the visual evaluation value of the three-dimensional shape of the skin surface used for estimation of the visual evaluation value is prepared in advance as follows. Keep it. The multiple regression equation created in this way is preferably stored so that it can be used for estimation of the visual evaluation value described later.

[0033] The multiple regression equation is, for example, a force that can be created by the following method, but is not limited to this method.

(1) Acquire the undulation value of skin with sufficient distribution of skin condition and age (hereinafter also referred to as “sample”), and calculate the obtained undulation value force roughness parameter and fractal dimension. The undulation value acquisition, roughness parameter, and fractal dimension calculation can be performed in the same manner as described above. The number of samples used at this time is 30 or more, preferably 50 or more, and more preferably 200 or more.

[0034] (2) Next, an evaluator suitable for representing a third party is prepared, the sample is presented, and the smoothness of the skin is visually evaluated. The sample used for this evaluation may be the skin itself or a photograph or image of the skin. When using photographs or images, it is preferable to use gray scale to remove the tint elements. These assessments may be absolute assessments such as scoring, but compared to another sample to ensure objectivity. Relative evaluation such as ranking is preferable. In ranking, if there is a difference, the ranking can be the same. In this case, in order to further ensure objectivity, it is preferable to explain that the evaluation is based only on skin elements that can be recognized only by visual observation. Here, an evaluator suitable for representing a third party may be of any age or gender as long as it can at least understand the meaning of “smoothness of skin”. The number of evaluators is usually 4 or more, preferably 10 or more.

 [0035] (3) It is preferable to repeatedly evaluate (2). The number of repetitions may be appropriately adjusted according to the number of evaluators. In order to obtain an objective evaluation result, the evaluation should be repeated usually 3 times or more, preferably 4 times or more, more preferably 5 times or more.

 (4) Next, the evaluation result of (3) is statistically processed, and a visual evaluation value of the smoothness of the skin surface is calculated for each sample. The visual evaluation value used in this statistical process may be the score obtained as it is, or when performing a relative evaluation by ranking, the smoothness of the skin is good even with the ranking itself. It may be a score given a high numerical value in ascending order. Further, these visual evaluation values may be a total for each sample or may be an average value. For example, when ranking is performed for n samples, the average value of the total score of each sample can be obtained by setting the i-th score to n i + 1 and can be used as the visual evaluation value. In addition, the average value and standard deviation force of the sample can also be obtained as a visual evaluation value by obtaining a deviation value of each sample, or these values can be divided into arbitrary stages to obtain a visual evaluation value.

 [0037] (5) Next, using the roughness parameter and fractal dimension obtained in (1) and age as explanatory variables, and the evaluation value of skin smoothness obtained in 4) as the objective variable, multiple regression analysis ( MRA) and obtain a multiple regression equation. Such a multiple regression analysis can be performed by an ordinary method, for example, using statistical processing software sold on the market! The roughness parameter used at this time may be one kind or plural kinds.

 [0038] (D—2) Estimation of visual evaluation value of smoothness of skin

The roughness parameter and fractal dimension of the skin surface of the subject obtained in (B) and (C) above, and the age of the subject were prepared in advance using the method of (D-1), etc. Substitute into the multiple regression equation indicating the relationship between the fractal dimension and age of the skin surface and the visual evaluation value of the smoothness of the skin, and estimate the visual evaluation value of the subject's skin smoothness. Estimated in this way The visual evaluation value of the smoothness of the applied skin can be displayed as it is, and it can be easily used in counseling and advice situations by processing it into data such as force deviation values and predefined ranks. It is preferable because it becomes a thing. For example, in the creation of the multiple regression equation used to estimate the visual evaluation value of the smoothness of the skin, the minimum and maximum values of the visual evaluation value of the sample used are equally divided into a plurality of arbitrary ranks. If the rank of the subject is displayed in alphabets or numbers, or in words indicating the level of smoothness, the visual evaluation value estimated for the subject can be displayed in terms of rank or language. .

 The estimation method of the present invention can be applied to the evaluation of an external preparation for skin. Among external preparations for skin, it can be suitably applied particularly to the evaluation of cosmetics.

 That is, the method for evaluating an external preparation for skin using the estimation method of the present invention (hereinafter also referred to as “the evaluation method for external preparation for skin of the present invention”) is used before and after the use of the external preparation for skin by the estimation method of the present invention. The visual evaluation value of the smoothness of the skin is estimated, and the result of the comparison of the visual evaluation values is used as an index to evaluate the external preparation for skin. Specifically, before the use of the external preparation for skin, the roughness parameter and fractal dimension of the subject's skin surface are calculated, and the roughness parameter and fractal dimension calculated according to the multiple regression equation prepared in advance and the age of the subject are calculated. Substituting and estimating the visual evaluation value of the smoothness of the skin before using the external preparation for skin. Then, the visual evaluation value of the smoothness of the skin is estimated in the same procedure after the use of the external preparation for skin, and the visual evaluation value of the smoothness of the skin before and after the use of the external preparation for skin is compared. If the visual evaluation value of the smoothness of the skin after use of the obtained external preparation for skin shows a better value than the visual evaluation value of the smoothness of the skin before use of the external preparation for skin, then the external preparation for skin Can be evaluated as having the effect of improving the smoothness of the skin, and the greater the degree of increase, the higher the effectiveness of the external preparation for skin against the smoothness of the skin. On the other hand, if the visual evaluation value of the smoothness of the skin after the use of the external preparation for skin is the same as or less than the visual evaluation value of the smoothness of the skin before the use of the external preparation for skin, The topical skin preparation can be evaluated as having an effect of not affecting the smoothness of the skin or reducing the smoothness of the skin.

The method for evaluating an external preparation for skin of the present invention varies in result even if the evaluator changes. There is an advantage that a change with a small change amount can be accurately captured.

 [0040] In order to carry out the estimation method of the present invention, it is preferable to use an apparatus for estimating the visual evaluation of the smoothness of skin described below (hereinafter also referred to as "estimation apparatus of the present invention"). The estimation apparatus of the present invention includes the following means.

 (A) Means to input subject's age

 (B) Means for obtaining the undulation value of the subject's skin surface

 (C) Means for calculating the roughness parameter of the skin surface

 (D) Means for calculating the undulation force force Fractal dimension of the skin surface

 (E) The calculated skin surface roughness of the test subject is expressed in a multiple regression equation indicating the relationship between the skin surface roughness parameter, the fractal dimension and age of the skin surface, and the visual evaluation value of the smoothness of the skin. A method to obtain a visual evaluation value of skin smoothness by substituting parameters, fractal dimensions, and age

 (F) Means for outputting the obtained visual evaluation value

 The definition of each term in the estimation apparatus of the present invention is the same as the definition in the estimation method of the present invention.

 Hereinafter, the best mode for carrying out the present invention will be specifically described with reference to the drawings. However, the following configuration is an example, and the present invention is not limited to the configuration of the embodiment. Figure 6 shows the input of the subject's age and the installation of a replica of the subject's skin surface. After obtaining the undulation value of the replica force skin surface, the roughness parameter and fractal dimension of the undulation value force skin surface are calculated. Substituting these numerical values and the input age into the multiple regression equation stored in the device to obtain a visual evaluation value of the smoothness of the skin and displaying it (estimation device a) It is a hardware block diagram of.

As shown in FIG. 6, the estimation device a includes an age input unit 1, an undulating value acquisition unit 2, a CPU (Central Processing Unit) 3, a ROM (Read Only Memory) 4, a RAM (Random Acces Memory) 5, and a magnetic disk. A device 6, a recording unit 7, an operation unit 8, and a display unit 9 are provided. They are connected to each other via a bus. The age input unit 1 is (A) a means for inputting the age of the subject, and can be an input device such as a keyboard or a microphone. The undulation value acquisition unit 2 (B) is a means for acquiring the undulation value of the subject's skin surface, such as a three-dimensional roughness meter It can be set as the apparatus which measures the undulation value of the surface of the skin. CPU3 includes (C) means for calculating the skin surface roughness parameter, (D) means for calculating the fractal dimension of the skin surface, and (E) a multiple regression equation to calculate the roughness of the subject's skin surface. This is a means of substituting parameters and fractal dimensions and age to obtain a visual evaluation value of the smoothness of the skin. According to the program stored in ROM4, the roughness parameter is calculated from the obtained relief values and the fractal dimension is obtained. And a process for calculating a visual evaluation value of the smoothness of the skin using the multiple regression equation stored in ROM4. ROM4 stores a program necessary for the function of the estimation device A and a multiple regression equation necessary for calculating a visual evaluation value. The RAM 5 temporarily stores a part of an OS (Operating System) program and application programs to be executed by the CPU 3. The magnetic disk device 6 is used as an external storage of the RAM 5 and has a recording unit 7. The operation unit 8 is operated when inputting necessary data such as a predetermined regression command. The display unit (F) is a means for outputting the obtained visual evaluation value, and can be, for example, a display device such as a liquid crystal display, an audio output device such as a speaker, or an output device such as a printer.

[0042] The present invention also provides a program that causes a computer, another device, a machine, or the like to execute part or all of the processing. The present invention also provides a program in which such a program is recorded on a recording medium readable by a computer or the like.

 Example

 [0043] Hereinafter, the present invention will be described in detail with reference to examples and the like. This does not limit the scope of the present invention.

[0044] <Create multiple regression equation>

 For the buttock of 248 women in their 10s and 50s (hereinafter also referred to as “samples”), the skin surface roughness parameters, the fractal dimension and age of the skin surface, and the visual evaluation value of the smoothness of the skin The relationship was analyzed.

 First, 20 minutes after washing the face, the digital camera (Nikon)

D100, lens: 60mm Macro lens) was taken at a distance of 20cm to obtain an image. A 1.5 cm square from the center of the region shown in Fig. 1 was cut out from this image with a resolution of 300 pixels, and used as a sample image. Next, using Asahi Biomed's silicon ABS-01-WW from 2cm * 2cm of the same site, a replica of the buttocks was collected, and then a highly accurate 3D image processing device LIP- from Science Systems Co., Ltd. Using the 50, the lcm * 1cm region in the center of the replica was scanned by 1000 laser scans at 10 m intervals in the y direction (vertical direction) (see Fig. 1) to obtain relief values. Next, since the sampling period in the X and y directions of LIP-50 is different from 9.4 / ζ πι and 10 / zm, respectively, what is the interval between the x and y directions using the Sine function? Was also supplemented to 10 m.

 This undulating force force is obtained by filtering the obtained cross section curve (see Fig. 7), and the roughness parameters Rmr, RSm, Ra, Rp, Rv, Rz, Rc, Rt, Rq, Rsk, Rk based on JIS B0601: 2001 u was calculated. The roughness parameters were obtained from the horizontal and vertical cross-sectional curves, respectively.

Further, the fractal dimension was calculated using a program for causing a computer to execute the box counting method. That is, in the box counting method, X = 1000, Y = 1000, h was changed to 2, 4, 8, 16 ^... 2 ^η, and N (h) was calculated for each h. Then, logN (h) was plotted against logh, and the fractal dimension was obtained for each sample.

 [0045] After taking a picture of the above, the five samplers evaluated the sample images visually. The image was grayscaled with Adobe Photoshop (registered trademark) of Adobe Systems Co., Ltd. (see FIG. 8) in order to remove the color element. Subsequently, this sample image is divided into 5 groups (50 people x 4 groups + 48 people XI group) so that each age is evenly included by random numbers, and each group, 5 evaluators in order of smooth skin. When ranking was performed, the scores were evenly distributed so that each group had 1 to 50 points with the smoothest to the least smoothness. The total score of each sample image was also averaged and used as a visual evaluation value for the smoothness of the skin.

[0046] Subsequently, the relationship between the roughness parameter calculated above and age was analyzed. As a result, Rmr obtained from Ra and the vertical cross-sectional curve monotonically increases with age, whereas RSm and horizontal cross-sectional curve force obtained Rmr peaked in the 30s. It became clear that showing. As a result, in a typical aging process, the unevenness structure with directionality based on the recession and sagging of the edge of the concavo-convex structure was observed in favor of the formation and expansion of pore-level concavo-convex on the skin surface. Appearance, that is, tin plate-like defects were confirmed (see Fig. 9). It was also found that among these roughness parameters, Rmr, especially Rmr in the horizontal direction, has a low relevance to the fractal dimension.

 Next, using the visual evaluation value (z) of the smoothness of the skin obtained above as the objective variable, the roughness parameter (a), fractal dimension (b), and age (c) of each skin surface obtained above were calculated. As an explanatory variable, multiple regression analysis (prediction formula) was obtained by conducting multiple regression analysis.

 As a result, it was found that when Ra, RSm or Rmr was used as the roughness parameter, a high correlation was obtained with the visual evaluation value of the smoothness of the skin. In particular, it was found that using the obtained cross-sectional curve force in the horizontal direction and the vertical direction Ra or the obtained cross-sectional curve force in the horizontal direction gave a high correlation with the visual evaluation value of the smoothness of the skin.

 The horizontal regression curve force The multiple regression equation showing the relationship between the obtained Rmr, fractal dimension and age, and the visual evaluation value of the smoothness of the skin,

 z (Visual evaluation value of skin smoothness) = 3. 77 * a (Rmr) + 117. 3 * b (fractal dimension) — 0. 379 * c (age) — 235. 62… (7)

 The multiple correlation coefficient was 0.706 and P <0.01.

 The multiple regression equation showing the relationship between the horizontal Ra, fractal dimension and age and the visual evaluation value of the smoothness of the skin is

 z (Visual evaluation of skin smoothness) = -0.694 * a (Ra) +83. 7 * b (fractal dimension) — 0. 388 * c (age) — 155. 63… (8 )

 The multiple correlation coefficient was 0.710 and P was 0.01.

 As shown in Equation (7) and Equation (8), it can be seen that the fractal dimension and the surface roughness parameter have a high correlation with the visual evaluation value of the smoothness of the skin.

 When using Ra in the vertical direction, a high multiple correlation coefficient was obtained as in the case of using Ra in the horizontal direction.

For comparison, a regression analysis (prediction formula) was performed by performing a regression analysis using the visual evaluation value (z) as an objective variable and the fractal dimension (b) of each skin surface obtained above as an explanatory variable. Obtained. The regression equation showing the relationship between the fractal dimension and the visual evaluation value of the smoothness of the skin is z (visual evaluation value of the smoothness of the skin) = — 162. 81b (fractal dimension) + 396. 96 · · · (9)

 And the correlation coefficient. 603, P <0. 001.

[0047] The visual evaluation value (Measured) of the smoothness of the skin of each of the 248 samples obtained by using the equations (7) and (9), and the visual evaluation value obtained by the evaluation by the evaluator Fig. 10 and Fig. 11 show the relationship (scatter diagram). From these figures, the visual evaluation value of the smoothness of the skin can be estimated using only the fractal dimension, but if the roughness parameter and age, which are extremely low in correlation with the fractal dimension, are further used, the skin can be accurately estimated. It can be seen that the visual evaluation value of the smoothness can be estimated.

<Example 1>

 Estimation of visual evaluation value of skin smoothness using replica

 For the five female subjects aged 18 to 53 years, not included in the 248 subjects, replicas of the buttocks were collected by the above method, and the roughness parameter and fractal dimension were calculated. ) And Eq. (8) were substituted for the calculated roughness parameter and fractal dimension, and the age of the female subject, and the visual evaluation value of the smoothness of the skin was estimated.

 Further, similarly to the above method, a visual evaluation value of the smoothness of the skin was obtained using the image of the buttock, and this visual evaluation value was estimated using the above formulas (7) and (8). Comparison with visual evaluation values (Table 1). From Table 1, it can be seen that the visual evaluation value estimated using Equation (7) and Equation (8) and the visual evaluation value obtained by visual evaluation of the image of the buttock are very close. From this, it can be seen that according to the estimation method of the present invention, the visual evaluation value of the smoothness of the skin can be estimated extremely simply and with high accuracy.

[0049] [Table 1] table 1

[0050] <Example 2>

 Evaluation of topical skin preparation

 Ten female subjects aged 30 to 50 years will be tested for one month using a skin external preparation containing a collagen fiber bundle remodeling agent shown in Table 2 below instead of the cream currently in use. went. Before and after the actual use test, replicas were collected from the heels of the skin, and the Rmr and fractal dimensions obtained for the horizontal cross-sectional curve force were calculated according to the method described above. Was substituted into Equation (7) obtained above to obtain an estimate of the visual evaluation value of the smoothness of the skin as seen by a third party. The estimated value of the smoothness of the skin as seen by the three people before the actual use test was 31.7 ± 9.0, but decreased to 27.9 ± 8.7 after the actual use test. There is a significant difference in some t-tests, P <0.001). From this, it was possible to evaluate that the external preparation for skin had an action of improving the smoothness of the skin.

[0051] [Table 2]

Table 2

 (A) POE (3) Cetinoreatenore 20% by mass Glycerin monostearate 10% by mass Flowable paraffin 100% by mass

2-Ethylhexanoic acid triglyceride 4 9% by weight Cetanol 50% by weight Benzyl ursolate 0 1% by weight Preservative 0.2% by weight

(B) Propylene glycol 1 0.0 mass% Purified water 5 7. 8 mass% Preparation method Combine the components of (A) and heat to 80 ° C. Combine the ingredients in (B) and heat to 80 ° C. While stirring the prescription of (A), add the prescription of (B) and emulsify with stirring, and then cool. Industrial applicability

 According to the present invention, anyone can easily and accurately estimate the smoothness of the skin visually observed by a third party. By evaluating the smoothness of the skin using the method of the present invention, useful information can be easily provided to the subject in the appropriate selection of skin counseling and cosmetics.

Claims

The scope of the claims

 [1] A process of obtaining the undulation value of the skin surface of the subject;

 A step of calculating a roughness parameter of the undulation force force skin surface;

 Calculating the fractal force force fractal dimension of the skin surface;

 In the multiple regression equation showing the relationship between the skin surface roughness parameter, the skin surface fractal dimension and age, and the visual evaluation value of the smoothness of the skin, the calculated roughness parameter and fractal dimension of the subject, And assigning age to obtain visual evaluation value of smoothness of skin

 A method for estimating a visual evaluation value of the smoothness of the skin.

 [2] The roughness parameter force is at least one of Rmr, RSm and Ra

The method of claim 1.

[3] The method according to claim 1 or 2, wherein the fractal dimension is calculated by a box counting method.

[4] The method according to claim 3, wherein the determining power of the box size in the box counting method is performed based on a standard deviation of the undulation value in a box.

[5] The method according to any one of claims 1 to 4, wherein the undulation value is also calculated as a skin replica force.

 [6] Estimate the visual evaluation value of the smoothness of the skin before and after using the skin external preparation by the method for estimating the visual evaluation value of the smoothness of the skin as described in any one of claims 1 to 5. And evaluating the external preparation for skin using the result of comparison of these visual evaluation values as an index.

[7] means to input the age of the subject;

 Means for obtaining the undulation value of the skin surface of the subject;

 Means for calculating a roughness parameter of the undulating force force,

 Means for calculating the fractal dimension of the skin surface from the undulation value;

In the multiple regression equation showing the relationship between the skin surface roughness parameter, the skin surface fractal dimension and age, and the visual evaluation value of the smoothness of the skin, the calculated roughness parameter and fractal dimension of the subject, Substituting age and visual evaluation value of smoothness of skin Means to obtain,

 Means for displaying the obtained visual evaluation value

 A visual evaluation value estimation device for the smoothness of the skin.

 Computer

 Means for calculating the skin surface roughness parameter from the undulation value of the subject's skin surface, means for calculating the fractal dimension of the skin surface from the undulation value of the subject's skin surface, a prepared skin surface roughness parameter, skin By substituting the calculated roughness parameter, fractal dimension, and age of the subject into the multiple regression equation showing the relationship between the surface fractal dimension and age and the visual evaluation value of the smoothness of the skin, the smoothness power of the skin Means for obtaining visual evaluation values

 Program for visual evaluation value of smoothness of skin for functioning as