JP6942012B2 - Skin evaluation method - Google Patents

Description

The present invention relates to a skin evaluation technique based on the surface shape of skin (skin).

Various methods for quantitatively evaluating the skin surface using images of the skin surface or three-dimensional shape data have been proposed.
In Patent Document 1, the brightness of a skin surface image obtained by irradiating a skin surface or a replica thereof with light is converted into a digital signal value having a multi-step hierarchy (gradation), and the brightness is converted into a digital signal value having a multi-step hierarchy (gradation). A skin surface shape measuring method that reads a luminance digital signal value, statistically processes the luminance digital signal value difference between each measurement point, and calculates the average value, standard deviation, median, and mode of the luminance difference as skin characteristic values is disclosed. There is.
Patent Document 2 discloses a skin surface analysis method that makes it possible to quantify the visual roughness of wrinkles and textures on the skin surface and the concealing effect of wrinkles and textures of skin cosmetics. In this analysis method, a two-dimensional sample image in which the fine light-dark distribution on the sample skin surface is emphasized is imaged, the two-dimensional sample image is processed, and then a one-dimensional light-dark profile is extracted and based on this one-dimensional light-dark profile. Calculate the average value, standard deviation and coefficient of variation of the distance between light and dark peaks.
Patent Document 3 discloses a technique for replacing the beauty of the appearance of the skin seen by a third party with an objective numerical value. In this technique, Sm (average interval of unevenness) and / or Pmr (load length ratio) and / or Ra (arithmetic), which are surface roughness parameters obtained from three-dimensional shape data obtained by measuring the unevenness state of a skin replica sample. Sm and / or Sm and / or of the skin replica sample to be differentiated by the regression equation (prediction equation) obtained from the correlation between the average roughness) and the skin beauty score obtained by subjectively judging the skin photograph. Apply Pmr and / or Ra to get a skin beauty score.

Japanese Unexamined Patent Publication No. 2-46833 Japanese Unexamined Patent Publication No. 6-189942 JP-A-2007-130295

However, each of the above methods has room for improvement in the analysis of the surface shape of the skin.
It is known that the surface shape of the skin has a multiple structure composed of a plurality of uneven elements having different dimensions. For example, on the surface of the skin, there are wrinkles, pores, texture formed from skin hills and skin grooves, uneven shape formed on the skin hill (hereinafter referred to as secondary relief), and the like. Have different dimensions. In each of the above-mentioned methods, since the treatment considering the multiple structure of the skin surface is not performed, there is room for improvement in the calculated index value related to the skin surface.
The present invention relates to a technique for evaluating skin based on the shape of the skin surface.

In the aspect of the present invention, the following configuration is adopted in order to solve the above-mentioned problems.
Aspects of the present invention relate to skin evaluation methods. The skin evaluation method includes a step of acquiring unevenness information on the skin surface measured from the skin or a replica of the skin, a decomposition step of extracting unevenness information in a predetermined spatial frequency band from the acquired unevenness information, and the extraction. It includes a calculation step of calculating a statistical value regarding the uneven shape from the unevenness information of a predetermined spatial frequency band, and an evaluation step of evaluating the skin using the calculated statistical value.
Another aspect of the present invention relates to, for example, a skin evaluation device and a computer (information processing device) that execute the above-mentioned skin evaluation method, and relates to a program that causes a computer to execute the above-mentioned skin evaluation method. It relates to a computer-readable storage medium on which such a program is recorded. This recording medium includes a non-temporary tangible medium.

According to the above aspect, it is possible to provide a technique for evaluating the skin with high accuracy based on the shape of the skin surface.

It is a flowchart which shows the skin evaluation method which concerns on 1st Embodiment. It is a figure which shows the example of the unevenness information of the skin surface. It is a flowchart which shows the skin evaluation method which concerns on 2nd Embodiment. It is a figure which shows the multiple structure of the skin surface. It is a figure which shows the multiple decomposition of the unevenness information of the skin surface in the 2nd Embodiment. It is a figure which conceptually shows the hardware composition example of the skin evaluation apparatus. It is a figure which conceptually shows the processing composition example of the skin evaluation apparatus. It is a figure which shows the relationship between the skin image of three sample skins, and the statistical value about the uneven shape obtained as a comparative example from each skin image. FIG. 9A is a graph showing the relationship between the arithmetic mean roughness (Ra) and fine wrinkles for each of the unevenness components of the four spatial frequency bands and all the unevenness components, and FIG. 9B is a graph showing the relationship between the four spatial frequencies. It is a graph which shows the relationship between the mean square root roughness (Rq) and fine wrinkles about each of the unevenness component of a band and all unevenness components. FIG. 10 is a graph showing the relationship between the skewness (Rsk) and the tactile sensation (smoothness) for each of the uneven components of the four spatial frequency bands and all the uneven components. FIG. 11 (a) is a graph showing the relationship between the change in skewness (Rsk) and the change in tactile sensation (smoothness) for each of the uneven components of the four spatial frequency bands and all the uneven components, and is shown in FIG. 11 (b). ) Is a graph showing the relationship between the change in kurtosis (Rku) and the change in tactile sensation (smoothness) for each of the uneven components of the four spatial frequency bands and all the uneven components.

Hereinafter, an example of a preferred embodiment of the present invention (hereinafter, simply abbreviated as the present embodiment) will be described. Each of the following embodiments is an example, and the present invention is not limited to the configuration of each of the following embodiments.

[First Embodiment]
The skin evaluation method according to the first embodiment will be described with reference to FIG.
FIG. 1 is a flowchart showing a skin evaluation method according to the first embodiment.
As shown in FIG. 1, the skin evaluation method according to the first embodiment includes a step (S11), a step (S13), a step (S15), and a step (S17).

The step (S11) is a step of acquiring unevenness information on the skin surface such as measured from the target skin or a replica of the target skin.
The unevenness information acquired in the step (S11) may include at least the height information of the skin surface, and specific data such as three-dimensional information including the height information and waveform information indicating the cross-sectional shape of the skin. The format is not restricted. The unevenness information may be unevenness waveform data which is two-dimensional information of the position information and height information of the skin surface, or may be image data indicating the height by brightness. For example, by measuring the target skin or a replica of the target skin using a confocal laser microscope or the like, it is possible to obtain data showing the three-dimensional shape of the skin surface, and by specifying a desired line in the data. , It is also possible to obtain a concave-convex shape profile (concave-convex waveform data) on the line. The device capable of acquiring unevenness information, that is, the device that can be used in the step (S11) is not limited to the confocal laser microscope, but is a three-dimensional measuring device of an optical system, a contact type or a non-contact type surface roughness. There are various measuring instruments such as measuring instruments, but it is preferable to use an instrument having a spatial frequency resolution of 1/20 (cycle / μm) or more and a resolution (detectability) in the height direction of 0.05 μm or less.

FIG. 2 is a diagram showing an example of unevenness information on the skin surface.
In the example of FIG. 2, a replica in which the skin surface shape is transferred from the cheek of the subject is collected, and an image (three-dimensional information) measured by a laser microscope from the replica is shown at the upper part. Further, a desired line is specified on the image, and uneven waveform data including position information and height information on the line is shown at the bottom.
In the example of FIG. 2, the unevenness information on the skin surface was measured from the skin replica, but it may be measured directly from the skin.

Skin replicas are collected from the skin surface of the site to be evaluated. The replica only needs to have the uneven shape of the skin surface transferred, and the generation method thereof is not limited. For example, a replica can be produced by pressing an agent such as a silicone impression material against the skin surface.
Further, in the example of FIG. 2, as the unevenness information of the skin surface, the unevenness waveform data acquired from the three-dimensional information (image) including the height information is used, but the three-dimensional information (image) is used as it is on the skin surface. It may be used as unevenness information.

The step (S13) is a decomposition step of extracting unevenness information in a predetermined spatial frequency band from the unevenness information acquired in the step (S11).
Here, the "predetermined spatial frequency band" means a spatial frequency band that has already been determined before the execution of the step (S13).
The “spatial frequency band” means a range of the number of height fluctuations per unit length (for example, 1 μm) suitable for the uneven shape of the skin surface.
As a predetermined spatial frequency band, it is desirable to set a spatial frequency band in which a correlation with the skin evaluation index is recognized. Further, a spatial frequency band corresponding to the size (scale) of a certain uneven element forming a multiple structure of the surface shape of the skin may be set. Since the scale of the uneven shape of the skin differs depending on the part, the spatial frequency band is appropriately selected according to the shape characteristics of the part to be measured. For example, at least a part of the band from 1/800 (cycle / μm) to 1/60 (cycle / μm) is set as the spatial frequency band corresponding to the uneven elements such as texture and skin hills in the uneven shape of the skin surface of the face. However, at least a part of the band from 1/100 (cycle / μm) to 1/20 (cycle / μm) may be set as the spatial frequency band corresponding to the uneven element of the secondary relief. , 1/400 (cycle / μm) or less may be set as the spatial frequency band corresponding to the concavo-convex element having a size larger than those concavo-convex elements.

Various methods can be used as a method for extracting unevenness information in a predetermined spatial frequency band in the step (S13). For example, the unevenness information acquired in the step (S11) is converted into a spatial frequency domain by using a Fourier transform or the like, a filter is applied in the spatial frequency domain, and then the original spatial domain is restored by using an inverse Fourier transform or the like. Therefore, it is possible to extract unevenness information in a predetermined spatial frequency band. Further, it is also possible to extract unevenness information in a predetermined spatial frequency band by using another frequency analysis method such as a wavelet transform, a 2CR filter, or a phase compensation type digital filter (Gaussian filter).

The step (S15) is a calculation step of calculating a statistical value regarding the shape of the unevenness from the unevenness information of the predetermined spatial frequency band extracted in the step (S13).
The statistical value calculated in the step (S15) is a parameter based on the deviation in the height direction of the skin surface, and is a combination of mean, standard deviation, variance, skewness, kurtosis, or any one or more of them. be.
For example, the arithmetic mean roughness (Ra) is calculated as an average. The arithmetic mean roughness (Ra) is indicated by the average value of the absolute values of the deviations from the average line at the reference length of the roughness curve.
For example, the root mean square roughness (Rq) is calculated as the standard deviation or variance. The root mean square roughness (Rq) is indicated by the value obtained by averaging the square of the deviation from the average line at the reference length of the roughness curve and taking the square root.
As the skewness, for example, skewness (Rsk), which is the cube average of the uneven waveform at the reference length dimensionless by the cube of the root mean square roughness (Rq), is calculated. According to this skewness (Rsk), the symmetry of the unevenness of the skin surface is shown.
As the kurtosis, for example, Kurtosis (Rku), which is the fourth average of the uneven waveforms at the reference length dimensionless by the square of the root mean square roughness (Rq), is calculated. According to this Kurtosis (Rku), the sharpness of the unevenness on the skin surface is shown.

The step (S17) is an evaluation step of evaluating the target skin using the statistical values calculated in the step (S15).
The present inventors pay attention to the unevenness information of a specific spatial frequency band in the unevenness information of the skin surface that can be obtained by various methods, and the statistical value of a specific kind and the skin in the unevenness information. We found that there was a high correlation with a certain evaluation index of. It can also be said that the evaluation index of the skin is strongly influenced by the state of a specific uneven element in the uneven element group forming the multiple structure of the surface shape of the skin.
For example, as described in Examples described later, there is a correlation between the tactile sensation of the skin and the skewness of the uneven shape in the spatial frequency band (1/500 (cycle / μm) or more and less than 1/80 (cycle / μm)). It has been found to be expensive. Further, it has been found that there is a high correlation between the arithmetic mean roughness (Ra) and the root mean square roughness (Rq) of the spatial frequency band (less than 1/500 (cycle / μm)) and the appearance of the skin.

As a result, in the step (S17), by using the above-mentioned statistical value of the uneven shape of the predetermined spatial frequency band, the target skin is set with an evaluation index (for example, tactile sensation, appearance, etc.) showing a high correlation with the statistical value. Can be evaluated. For example, the tactile sensation of the target skin can be evaluated using the skewness calculated from the unevenness information of the spatial frequency band (1/500 (cycle / μm) or more and less than 1/80 (cycle / μm)). In addition, the arithmetic average roughness (Ra) and the root mean square roughness (Rq) calculated from the unevenness information of the spatial frequency band (less than 1/500 (cycle / μm)) are used to give an impression of the appearance of the target skin. Can be evaluated. Of course, the skin may be evaluated by a plurality of evaluation indexes such as appearance and touch. Moreover, the evaluation index of the skin is not limited to these tactile sensations and appearances.

The execution subject of the process (S17) may be a person or a computer.
For example, the steps (S11), steps (S13), and steps (S15) are executed by a computer, and the statistical values calculated in the steps (S15) are output from the computer via a display device, a printing device, or the like. In this case, a person can refer to the output statistical value, compare the statistical value with the comparison table, and evaluate the target skin. In the comparison table, for example, a range of statistical values is set for each classification of evaluation results of a certain evaluation index of skin (for example, 3 classifications such as good appearance, standard and bad appearance, 2 classifications such as clean and not clean). It may be a table. This comparison table can be generated for each age, gender, and potential celebrity, and may be pre-printed or output from a computer.
As another example, when the step (S17) is executed by the computer, the computer may calculate the evaluation score from the index value calculated in the step (S15) and output the evaluation score. The evaluation score may be a binary value of good or bad, a numerical value of 3 values or more, and a character string corresponding to the evaluation value ("good", "bad", "standard", "clean". , Etc.). The calculation method of the evaluation score by the computer will be described later.

As described above, in the first embodiment, the unevenness information of the skin surface measured from the skin or the skin replica is acquired, and the statistical value of the unevenness information of the predetermined spatial frequency band extracted from the unevenness information is used. The skin is evaluated. That is, according to the first embodiment, the characteristics of a specific uneven element in the uneven element group forming the multiple structure of the surface shape of the skin can be referred to in a limited manner.
Therefore, the skin can be evaluated with high accuracy by paying attention to a specific uneven element with respect to the skin surface shape having a multiple structure.

[Second Embodiment]
In the first embodiment described above, the unevenness information of a specific spatial frequency band is used, but in the second embodiment, the unevenness information of a plurality of spatial frequency bands multiple-decomposed from the unevenness information of the skin surface is used. .. Hereinafter, the skin evaluation method according to the second embodiment will be described with reference to FIG.
FIG. 3 is a flowchart showing a skin evaluation method according to the second embodiment.
As shown in FIG. 3, the skin evaluation method according to the second embodiment includes a step (S31), a step (S33), a step (S35), and a step (S37).
The step (S31) is the same as the above-mentioned step (S11).

The step (S33) is a step of decomposing the unevenness information acquired in the step (S31) into a plurality of spatial frequency bands. That is, the predetermined spatial frequency band decomposed in the step (S33) is at least two bands.
Here, decomposing the unevenness information measured from the skin or the skin replica into the unevenness information of a plurality of spatial frequency bands may be referred to as "multiple decomposition".
The plurality of spatial frequency bands are predetermined before the execution of the step (S33). The plurality of spatial frequency bands may include a predetermined spatial frequency band described in the first embodiment. Further, the plurality of spatial frequency bands may include a spatial frequency band that partially overlaps with other spatial frequency bands.
The plurality of spatial frequency bands can also be determined based on the representative dimensions (representative scale) of any of the plurality of uneven elements forming the multiple structure of the surface shape of the skin. Further, the correlation with the skin evaluation index may be investigated while changing the division of the spatial frequency band, and the plurality of spatial frequency bands may be determined based on the height of the correlation with the evaluation index.

FIG. 4 is a diagram showing a multi-layered structure on the skin surface. FIG. 5 is a diagram showing multiple decompositions of unevenness information on the skin surface in the second embodiment.
In the example of FIG. 5, the unevenness information of the skin surface acquired in the step (S31) is shown as unevenness waveform data at the uppermost part of FIG. By multiplexing the uneven waveform data in the step (S33), the concave-convex waveform data (concavo-convex information) of the four spatial frequency bands is extracted. Specifically, a spatial frequency band of 1/28 (cycle / μm) or more, a spatial frequency band of 1/80 (cycle / μm) or more and less than 1/28 (cycle / μm), 1/500 (cycle / μm) It is multiplexed into four spatial frequency bands: a spatial frequency band of less than 1/80 (cycle / μm) and a spatial frequency band of less than 1/500 (cycle / μm). Each graph of FIG. 5 is shown in a wavelength range corresponding to the spatial frequency band.

Here, as shown in FIG. 4, the uneven elements forming the multiple structure on the skin surface include textures composed of skin hills and skin grooves, aggregates and pores of textures having a size larger than the textures, and textures. At least a small secondary relief is included. When these three concave-convex elements are taken into consideration, of the four concave-convex waveforms exemplified in FIG. 5, two concave-convex waveforms in the spatial frequency band of less than 1/28 (cycle / μm) and 1/80 (cycle / μm) or more are obtained. Corresponding to the representative dimensions of the next relief, the uneven waveform in the spatial frequency band of less than 1/80 (cycle / μm) and 1/500 (cycle / μm) or more is associated with the representative dimensions of the texture, 1/500 (cycle / μm). Concavo-convex waveforms in the spatial frequency band less than) can be associated with representative dimensions of texture aggregates and pores.
As a result, in the example of multiple decomposition shown in FIG. 5, it can be considered that the unevenness information corresponding to each unevenness element forming the multiple structure on the skin surface is extracted.

The step (S35) is a step of calculating a plurality of types of statistical values from the unevenness information of any one or more of the plurality of spatial frequency bands decomposed in the step (S33).
The plurality of types of statistical values calculated in the step (S35) are parameters based on the deviation in the height direction of the skin surface, as in the first embodiment. Here, a plurality of combinations of mean, standard deviation, variance, skewness, or kurtosis are calculated as a plurality of kinds of statistical values.
Further, the calculated statistical values of the plurality of types may be calculated from the unevenness information of all the spatial frequency bands multiple-decomposed in the step (S33), or from the unevenness information of some of the plurality of spatial frequency bands. It may be calculated. That is, in the step (S35), the mean, standard deviation, variance, skewness, or kurtosis may be obtained in a part of the multiple-decomposed bands.
Further, each statistical value may be calculated from the unevenness information of the spatial frequency band different for each type of statistical value. For example, the skewness is calculated from the unevenness information of the predetermined first spatial frequency band, and the average, variance (or standard) is calculated from the unevenness information of the predetermined second spatial frequency band whose band is different from that of the first spatial frequency band. Deviation) and skewness may be calculated. However, a part of the calculated statistical values of the plurality of types may be calculated from the unevenness information of the common spatial frequency band.

As illustrated in Examples, the present inventors investigated which kind of statistical value in which spatial frequency band is highly correlated with each of a plurality of skin evaluation indexes, and highly correlated with the skin evaluation index. We have clarified the existence of multiple types of statistical values in a plurality of predetermined spatial frequency bands.
As a result, in the second embodiment, based on the correlation between the skin evaluation index found in this way and the statistical value of the unevenness information of each spatial frequency band, what kind of statistics are obtained from the unevenness information of which spatial frequency band. It is desirable to determine whether the value should be calculated.
Specifically, according to the embodiment, the skewness is calculated from the uneven waveform data of the spatial frequency band of 1/500 (cycle / μm) or more and less than 1/80 (cycle / μm), and the skewness is calculated to be 1/500 (cycle / μm). The mean and variance (or standard deviation) are calculated from the uneven waveform data in the spatial frequency band of less than μm). As shown in this example, it is desirable that the ratios of the upper limit frequency and the lower limit frequency of any one or more of the spatial frequency bands used in the calculation of the plurality of kinds of statistical values in the step (S35) are different from each other.

The step (S37) is a step of evaluating the target skin using a plurality of types of statistical values calculated in the step (S35). The evaluation method and the execution subject in the step (S37) are the same as those in the step (S17) in the first embodiment except that a plurality of types of statistical values are used. For example, the above-mentioned comparison table may be provided for each type of statistical value.
Further, in the step (S37), the target skin may be evaluated by one evaluation index using a plurality of types of statistical values, or the target skin may be evaluated by a plurality of evaluation indexes. In the former case, for example, it is a multiple regression equation obtained in advance by multiple regression analysis using the skin sample data of the population, and multiple kinds of statistical values (for example, skewness and kurtosis) are used as explanatory variables for the skin. The skin evaluation score can be calculated by substituting the plurality of types of statistical values calculated in the step (S35) into the multiple regression equation with the evaluation score (for example, the degree of smoothness) as the objective variable.
In the latter case, for example, by associating the types of skin evaluation indexes with each type of statistical value, it is possible to evaluate the target skin with a plurality of indexes using a plurality of types of index values.

As described above, in the second embodiment, the unevenness information of the skin surface measured from the target skin or a replica thereof is divided into a plurality of spatial frequency bands, and the plurality of irregularities calculated from the unevenness information of any one of a plurality of spatial frequency bands. Target skin is evaluated using species statistics. That is, according to the second embodiment, the surface shape of the skin having a multiple structure can be decomposed into the features of a plurality of uneven elements and can be referred to individually. By evaluating the skin based on each feature of the uneven element referred to individually in this way, the evaluation accuracy of the skin can be improved.

The skin evaluation method according to the first embodiment and the second embodiment can be executed by the following skin evaluation device. Hereinafter, the skin evaluation device will be described with reference to FIGS. 6 and 7.
FIG. 6 is a diagram conceptually showing a hardware configuration example of the skin evaluation device 10, and FIG. 7 is a diagram conceptually showing a processing configuration example of the skin evaluation device 10.

The skin evaluation device 10 is a so-called computer, and includes, for example, a CPU (Central Processing Unit) 11, a memory 12, an input / output interface (I / F) 13, a communication unit 14, and the like, which are connected to each other by a bus. The number of each hardware element forming the skin evaluation device 10 is not limited, and these hardware elements can also be collectively referred to as an information processing circuit. Further, the skin evaluation device 10 may include hardware elements (not shown in FIG. 6), and the hardware configuration thereof is not limited.

In addition to a general CPU, the CPU 11 may be composed of an integrated circuit (ASIC) for a specific application, a DSP (Digital Signal Processor), a GPU (Graphics Processing Unit), or the like.
The memory 12 is a RAM (Random Access Memory), a ROM (Read Only Memory), and an auxiliary storage device (hard disk or the like).
The input / output I / F 13 can be connected to a user interface device such as an output device 15 and an input device 16. The output device 15 is at least one of a device such as an LCD (Liquid Crystal Display) or a CRT (Cathode Ray Tube) display that displays a screen corresponding to drawing data processed by a CPU 11 or the like, a printing device, and the like. The input device 16 is a device that receives input of a user operation such as a keyboard and a mouse. The output device 15 and the input device 16 may be integrated and realized as a touch panel.
The communication unit 14 communicates with another computer via a communication network, exchanges signals with other devices, and the like. A portable recording medium or the like may also be connected to the communication unit 14. Further, a measuring instrument (laser microscope or the like) for measuring unevenness information from the target skin or a replica thereof may be connected to the communication unit 14.
Further, the skin evaluation device 10 may be a device having a built-in measuring device thereof.

The skin evaluation device 10 includes an acquisition unit 21, a disassembly unit 22, a calculation unit 23, an output processing unit 24, and the like. These are software elements, and are realized, for example, by loading and executing the evaluation program 19 stored in the memory 12 in the CPU 11. The evaluation program 19 is installed in the memory 12 from a portable recording medium such as a CD (Compact Disc), a memory card, or the like, or from another computer on the network via the input / output I / F 13 or the communication unit 14. It may be stored.

The acquisition unit 21 executes the step (S11) or the step (S31).
The acquisition unit 21 can acquire the unevenness information of the skin surface measured from the target skin or a replica thereof from the measuring instrument, an external computer, a portable recording medium, or the like via the input / output I / F 13 or the communication unit 14. .. Further, when the acquisition unit 21 is realized as a measuring instrument, the acquisition unit 21 may measure and generate the unevenness information thereof.

The disassembling unit 22 executes the step (S13) or the step (S33). The method of extracting unevenness information in a predetermined spatial frequency band and the method of multiple decomposition by the decomposition unit 22 are as described above.
The calculation unit 23 executes the step (S15) or the step (S35). The method for calculating the statistical value by the calculation unit 23 is also as described above.

The output processing unit 24 outputs skin evaluation information using one or more statistical values calculated by the calculation unit 23. For example, the output processing unit 24 executes the step (S17) or the step (S37). The specific content of the evaluation information output by the output processing unit 24 and the method of generating the evaluation information are not limited.
For example, the output processing unit 24 may hold a simple regression equation or a multiple regression equation obtained by regression analysis using the skin sample data of the population. The simple regression expression may be a regression expression using any one kind of statistical value as an explanatory variable and the skin evaluation score as an objective variable, and the multiple regression expression may use a plurality of types of statistical values as an explanatory variable and set the skin evaluation score as an explanatory variable. Any regression equation may be used as the objective variable. For example, the output processing unit 24 can hold a multiple regression equation with skewness and kurtosis as explanatory variables and skin smoothness as objective variables. The output processing unit 24 can calculate the skin evaluation score by substituting the statistical value calculated by the calculation unit 23 into such a regression equation, and output the evaluation score as the skin evaluation information. Of course, the output evaluation information may not be a numerical value, or may be an evaluation character string (“good”, “bad”, etc.) or a symbol corresponding to the numerical value in addition to the numerical value.
Further, the output processing unit 24 can also output the statistical value calculated by the calculation unit 23 and the above-mentioned comparison table as skin evaluation information. In this case, the person who actually evaluates the skin is the person who refers to the output evaluation information.

[Modification example]
In the above-described embodiment, the skin is evaluated using one or more statistical values of the uneven shape of the target skin, but the skin condition change (condition improvement, condition deterioration, etc.) is evaluated based on the change in the statistical value. You can also.
As illustrated in the examples, the present inventors have found that there is a correlation between changes in statistical values and changes in the state of skin at a certain evaluation index.
Therefore, with the continuous use of the external preparation for skin, the unevenness information measured from the target skin or its replica is acquired, one or more statistical values are calculated in the same manner as in the above-described embodiment, and the difference between the statistical values is used. Therefore, it is possible to evaluate the change status of the skin condition. For example, the range of the statistical value difference is obtained in advance for each change state (improvement, deterioration, no change) of the skin condition based on the sample skin, and which range the statistical value difference obtained for the target skin of the subject belongs to. It is possible to determine the state of change of the target skin such as improved, deteriorated, or no change. The change state of the skin condition may be calculated not only as a three-value such as improvement, deterioration, and no change, but also as a two-value of improvement and deterioration, or a change score of four or more values.

Therefore, it is also possible to realize a method for evaluating the effects of external preparations for skin such as cosmetics, pharmaceuticals, quasi-drugs, medical devices, beauty devices, etc., by using the skin evaluation method according to the above-described embodiment. In other words, the process of acquiring the unevenness information of the skin surface measured from the skin or the skin replica before and after applying the external preparation for skin, and the decomposition of extracting the unevenness information of a predetermined spatial frequency band from the acquired unevenness information. A step, a calculation step of calculating a statistical value regarding the uneven shape from the extracted unevenness information of a predetermined spatial frequency band, and a step of calculating the difference between the statistical values calculated before and after applying the external preparation for skin, and calculation. It is possible to realize an evaluation step of evaluating the skin before and after applying the external preparation for skin using the difference obtained, and a method for evaluating the effect of the external preparation for skin including.

Further, in the plurality of flowcharts used in the above description, a plurality of steps (processes) are described in order, but the execution order of each step is not limited to the order of description. The order of the illustrated steps can be changed within a range that does not hinder the contents.

Part or all of the above content may also be specified as follows. However, the above contents are not limited to the following descriptions.

<1> The process of acquiring the unevenness information of the skin surface measured from the skin or the replica of the skin, and
A decomposition step of extracting unevenness information in a predetermined spatial frequency band from the acquired unevenness information, and
A calculation step of calculating statistical values related to the uneven shape from the extracted uneven information of a predetermined spatial frequency band, and
An evaluation process for evaluating the skin using the calculated statistical values, and
Skin evaluation method including.

<2> The statistical value calculated in the calculation step includes at least skewness or kurtosis.
In the evaluation step, the skin is evaluated using at least the skewness or kurtosis calculated in the calculation step.
The skin evaluation method according to <1>.
<3> In the decomposition step, the predetermined spatial frequency band is set to at least two bands.
In the calculation step, the mean, standard deviation, variance, skewness, or kurtosis is obtained for each band.
The skin evaluation method according to <1>.
<4> In the decomposition step, the acquired unevenness information is multiple-decomposed into a plurality of different spatial frequency bands including the predetermined spatial frequency band.
In the calculation step, a plurality of types of statistical values are calculated from the unevenness information of any of the plurality of spatial frequency bands obtained by the plural decomposition.
In the evaluation step, the skin is evaluated using the calculated plurality of types of statistical values.
The skin evaluation method according to any one of <1> to <3>.
<5> In the decomposition step, the acquired unevenness information is multiple-decomposed into the plurality of spatial frequency bands including different predetermined first spatial frequency bands and predetermined second spatial frequency bands.
In the calculation step, the skewness is calculated from the unevenness information of the predetermined first spatial frequency band, and the kurtosis is calculated from the unevenness information of the predetermined second spatial frequency band.
In the evaluation step, the skin is evaluated using the calculated skewness and kurtosis.
The skin evaluation method according to <4>.
<6> In the evaluation step, the multiple regression equation obtained in advance by the multiple regression analysis using the skin sample data of the population is used, and the degree of smoothness of the skin is aimed at by using the skewness and the kurtosis as explanatory variables. The degree of skin smoothness is determined by using a multiple regression equation as a variable.
The skin evaluation method according to <5>.
<7> In any one of the plurality of spatial frequency bands used for calculating the plurality of types of statistical values in the calculation step, the ratio of the upper limit frequency and the lower limit frequency is different from each other.
The skin evaluation method according to any one of <4> to <6>.
<8> An acquisition means for acquiring information on the unevenness of the skin surface measured from the skin or a replica of the skin, and
Decomposition means for extracting unevenness information in a predetermined spatial frequency band from the acquired unevenness information, and
A calculation means for calculating statistical values related to the shape of unevenness from the extracted information on unevenness in a predetermined spatial frequency band, and
An output processing means that outputs the evaluation information of the skin using the calculated statistical value, and
A skin evaluation device equipped with.
<9> The calculation means calculates at least the skewness or kurtosis of the uneven shape as the statistical value.
The output processing means generates evaluation information of the skin using at least the skewness or kurtosis calculated by the calculation means.
The skin evaluation device according to <8>.
<10> The decomposition means multiplexizes the acquired unevenness information into a plurality of different spatial frequency bands including the predetermined spatial frequency band.
The calculation means calculates a plurality of types of statistical values from the unevenness information of any of the plurality of spatial frequency bands obtained by the plural decomposition.
The output processing means generates the evaluation information of the skin using the calculated plurality of kinds of statistical values.
The skin evaluation device according to <8> or <9>.
<11> The decomposition means multiplexizes the acquired unevenness information into the plurality of spatial frequency bands including different predetermined first spatial frequency bands and predetermined second spatial frequency bands.
The calculation means calculates the skewness from the unevenness information of the predetermined first spatial frequency band, calculates the kurtosis from the unevenness information of the predetermined second spatial frequency band, and calculates the kurtosis.
The output processing means uses the calculated skewness and kurtosis to generate the skin evaluation information.
The skin evaluation device according to <10>.
<12> The output processing means is a multiple regression equation previously acquired by multiple regression analysis using population skin sample data, and the degree of smoothness of the skin is determined by using the skewness and the kurtosis as explanatory variables. The degree of skin smoothness is determined by using the multiple regression equation as the objective variable.
The skin evaluation device according to <11>.
<13> In any of the plurality of spatial frequency bands used in the calculation means for calculating the plurality of types of statistical values, the ratio of the upper limit frequency and the lower limit frequency is different from each other.
The skin evaluation device according to any one of <10> to <12>.

Examples will be given below, and the above contents will be described in more detail. However, the description of the following examples does not impose any limitation on the above contents.

In this embodiment, in order to verify the effect of the above-described embodiment, the skins of a plurality of subjects are used as sample skins, and the appearance (appearance) of each sample skin is based on the unevenness information measured from each sample skin replica. ) And the relationship between the tactile sensation and the statistical values regarding the uneven shape of the skin were investigated.

Replicas of each sample skin were generated by pressing a hydrophilic vinyl silicone impression agent against the area from the side of the nose to the cheeks of each subject. Therefore, it was transferred to the replica of each sample skin in a state where the uneven shape of the skin surface of the target site of each subject was inverted.
The three-dimensional shape of the skin transfer surface of the replica of each sample skin collected in this way is measured with a laser microscope at a magnification of 10 times, and a skin image showing height information (unevenness information) for each sample skin in color is obtained. Each was generated. Further, a line of about 7 mm was designated for the skin image of each sample skin, and uneven waveform data showing the relationship between the height and the position on the line was acquired.

FIG. 8 is a diagram showing the relationship between the skin images of the three sample skins and the statistical values regarding the uneven shape obtained from each skin image as a comparative example.
In the comparative example, the statistical value regarding the uneven shape was calculated from the entire uneven waveform data without performing the multiple decomposition as in the above-described embodiment. Specifically, by designating 20 lines for each skin image of sample skins A, B, and C, 20 uneven waveform data are acquired for each sample skin, and statistics of each uneven waveform data are obtained. The average value of the values is shown in FIG. As statistical values, as shown in FIG. 8, arithmetic mean roughness (Ra), root mean square roughness (Rq), skewness (skewness) (Rsk), and kurtosis (Rku) were calculated. ..
Comparing the statistical values calculated as comparative examples with the skin images of each sample skin, it can be seen that the uneven shape of the skin cannot be accurately indexed in any of the statistical values. In particular, as shown in the skin image, although the surface shapes of the sample skin A and the sample skin B are clearly different, all the statistical values show such a difference in the surface shape. It can not be said.

In this embodiment, the above-mentioned plurality of subjects are made to use a common skin external preparation, and the skin before and after the use of the skin external preparation is used as sample skin, respectively, and as described above from the replica of each sample skin. Concavo-convex waveform data was acquired by a laser microscope. Then, by multiplexing the uneven waveform data of each sample skin into four spatial frequency bands, the uneven waveform data of each of the four spatial frequency bands is extracted, and four kinds of index values (Ra, Rq) are extracted for each spatial frequency band. , Rsk, Rku) were calculated. Then, the difference between the index values calculated for the sample skin before the use of the external preparation for skin and the sample skin after the use was calculated for each subject. As a result, for each subject, four kinds of index value differences in each of the four spatial frequency bands were acquired.
The four spatial frequency bands are a spatial frequency band of 1/28 (cycle / μm) or more, a spatial frequency band of 1/80 (cycle / μm) or more and less than 1/28 (cycle / μm), and 1/500 (cycle / μm). It is a spatial frequency band of μm) or more and less than 1/80 (cycle / μm), and a spatial frequency band of less than 1/500 (cycle / μm).

On the other hand, in this example, the appearance (appearance) and tactile sensation of the skin before and after the use of the external preparation for skin in the plurality of subjects were scored by sensory evaluation.
The appearance was evaluated by a method in which a professional evaluator visually evaluated the sample skin to perform a sensory evaluation of the conspicuousness of fine wrinkles on a scale of 10 (referred to as a fine wrinkle score). The term "fine wrinkles" here means wrinkles that disappear when moisturized.
The tactile sensation was evaluated by a method in which a professional evaluator palpates the sample skin and sensory evaluates the degree of smoothness on a scale of 5 (referred to as tactile sensation score).
Furthermore, the difference between the evaluation score of the skin before the use of the external preparation for skin and the evaluation score of the skin after the use of the external preparation for the same subject was calculated. Specifically, the difference between the fine wrinkle score of the skin before use and the fine wrinkle score of the skin after use was calculated, and the difference between the tactile sensation score of the skin before use and the tactile sensation score of the skin after use was calculated.

FIG. 9A is a graph showing the relationship between the arithmetic mean roughness (Ra) and fine wrinkles for each of the unevenness components of the four spatial frequency bands and all the unevenness components, and FIG. 9B is a graph showing four. It is a graph which shows the relationship between the mean square root roughness (Rq) and fine wrinkles about each of the unevenness component of a spatial frequency band and all unevenness components. 9 (a) and 9 (b) show information about the skin after use of the external preparation for skin.
In FIG. 9, the horizontal axis shows the unevenness component of the four spatial frequency bands (indicated by the corresponding wavelength range) and all the unevenness components (indicated as “whole”), and the vertical axis shows each statistical value. The "good" bar graph shows the group of subjects whose fine wrinkles are less noticeable than the standard, the "standard" bar graph shows the group of subjects whose fine wrinkles are more noticeable than the standard, and the "bad" bar graph shows the subjects whose fine wrinkles are more noticeable than the standard. Shows a group of. The length of each bar graph indicates the average of the statistical values for each subject group, and the vertical line superimposed on each bar graph indicates the standard error of the statistical values for each subject group.

According to FIGS. 9 (a) and 9 (b), the arithmetic mean roughness (Ra) and the root mean square roughness (Rq) with respect to the uneven component of the spatial frequency band of less than 1/500 (cycle / μm) are It is confirmed that there is a correlation with the conspicuity of fine wrinkles.
As a result, the appearance of the subject's skin changes due to the difference in statistical values of the arithmetic mean roughness (Ra) and the root mean square roughness (Rq) for the uneven component in the spatial frequency band of less than 1/500 (cycle / μm). Has been proven to be able to be estimated.

FIG. 10 is a graph showing the relationship between the skewness (Rsk) and the tactile sensation (smoothness) for each of the uneven components of the four spatial frequency bands and all the uneven components. FIG. 10 shows information about the skin after the use of the external preparation for skin.
In FIG. 10, the horizontal axis indicates the unevenness component of the four spatial frequency bands (indicated by the corresponding wavelength range) and all the unevenness components (indicated as “whole”), and the vertical axis indicates each statistical value. The "good" bar graph shows the group of subjects with better than standard tactile sensation (smoothness), the "standard" bar graph shows the group of subjects with standard tactile sensation (smoothness), and the "bad" bar graph shows the group of subjects with standard tactile sensation (smoothness). Shows a group of subjects whose (smoothness) is worse than normal. The length of each bar graph indicates the average of the statistical values for each subject group, and the vertical line superimposed on each bar graph indicates the standard error of the statistical values for each subject group.

According to FIG. 10, it is confirmed that the skewness of the unevenness component in the spatial frequency band of less than 1/80 (cycle / μm) and 1/500 (cycle / μm) or more correlates with the tactile score.
This demonstrates that the tactile sensation (smoothness) of the subject's skin can be estimated from the skewness of the unevenness component in the spatial frequency band of 1/500 (cycle / μm) or more and less than 1/80 (cycle / μm). ..
Furthermore, the smaller the skewness of the uneven component in the spatial frequency band of 1/500 (cycle / μm) or more and less than 1/80 (cycle / μm), the better the tactile sensation (smoothness). Has been found. However, even if the skewness is large, it may not always be judged that the tactile sensation is good. Therefore, it may be possible to evaluate the tactile sensation with higher accuracy by further considering other statistical values (for example, mean, variance (and standard deviation), kurtosis, etc.) for the uneven shape of the skin.

FIG. 11 (a) is a graph showing the relationship between the change in skewness (Rsk) and the change in tactile sensation for each of the uneven components of the four spatial frequency bands and all the uneven components, and FIG. 11 (b) shows four. It is a graph which shows the relationship between the change of the kurtosis (Rku) and the change of the tactile sensation about each of the uneven component of the spatial frequency band and all the uneven components.
In FIG. 11, the horizontal axis shows the unevenness component of the four spatial frequency bands (indicated by the corresponding wavelength range) and all the unevenness components (indicated as “whole”), and the vertical axis shows the difference between the statistical values (after use). The value obtained by subtracting the statistical value before use from the statistical value) is shown. The "+" bar graph indicates the subject group with improved tactile sensation (smoothness), the "±" bar graph indicates the subject group with no change in tactile sensation, and the "-" bar graph indicates the subject group with deteriorated tactile sensation. .. The length of each bar graph indicates the average of the statistical value differences for each subject group, and the vertical lines superimposed on each bar graph indicate the standard error of the statistical value differences for each subject group.

According to FIGS. 11 (a) and 11 (b), the change in skewness with respect to the unevenness component in the spatial frequency band of less than 1/80 (cycle / μm) and 1/500 (cycle / μm) or more, and 1 /. It is confirmed that the change in kurtosis with respect to the uneven component in the spatial frequency band of less than 500 (cycle / μm) correlates with the change in the tactile score.
Regarding the difference in the statistical values of the skewness (Rsk), the group of subjects with improved tactile sensation showed a negative value, and the group of subjects with deteriorated tactile sensation showed a positive value. This indicates that the skewness of the subjects with improved tactile sensation decreased after the use of the external preparation for skin, and conversely, the skewness of the subjects with deteriorated tactile sensation increased. ing. That is, the result is based on the logic that the smaller the skewness of the uneven shape on the skin surface, the better the smoothness.
On the other hand, the difference in the statistical value of kurtosis (Rku) is the opposite of the skewness. That is, for the group of subjects with improved tactile sensation, it was shown that the kurtosis was increased after the use of the external preparation for skin, and for the group of subjects with deteriorated tactile sensation, conversely, it was shown that the kurtosis was decreased. There is.
As a result, the change in skewness related to the unevenness component of the spatial frequency band of 1/500 (cycle / μm) or more and less than 1/80 (cycle / μm), and the spatial frequency band of less than 1/500 (cycle / μm) It has been demonstrated that the change in the tactile sensation of the subject's skin can be estimated with high accuracy by the change in the kurtosis related to the unevenness component.
Furthermore, the smaller the skewness of the uneven component in the spatial frequency band of 1/500 (cycle / μm) or more and less than 1/80 (cycle / μm), the better the tactile sensation (smoothness), and the better the tactile sensation (smoothness). It has also been newly found that the greater the kurtosis of the uneven component in the spatial frequency band of less than cycle / μm), the better the tactile sensation (degree of smoothness). However, even if the kurtosis is large, it may not always be judged that the tactile sensation is good. Therefore, when evaluating the tactile sensation based on the kurtosis of the uneven shape of the skin, it may be possible to evaluate the tactile sensation with higher accuracy by further considering other statistical values (for example, Rq, dispersion value, skewness, etc.). ..
Furthermore, by performing multiple regression analysis of the relationship between the change in kurtosis [Rku (500 μm-)] and the change in skewness [Rsk (80-500 μm)] and the change in tactile score, the change in tactile score is measured. It was confirmed that it can be estimated from the change in kurtosis and the change in skewness. The following multiple regression equation was calculated by the multiple regression analysis (coefficient of determination: 0.146).
Change in tactile score = 4.01 x [Rsk (80-500 μm)] -0.73 x [Rku (500 μm-) -0.25

In this embodiment, the uneven waveform in the spatial frequency band of less than 1/28 (cycle / μm) and 1/80 (cycle / μm) or more is associated with the representative dimension of the secondary relief, and is less than 1/80 (cycle / μm). Concavo-convex waveforms in the spatial frequency band of 1/500 (cycle / μm) or more are associated with typical texture dimensions, and uneven waveforms in the spatial frequency band of less than 1/500 (cycle / μm) are representative of texture aggregates and pores. Corresponds to dimensions. It was verified that the shape of the skin of each dimension can be evaluated by dividing these spatial frequency bands and expressing them by mean, standard deviation, variance, skewness, or kurtosis.

10 Skin evaluation device 11 CPU
12 Memory 13 I / O I / F
14 Communication unit 15 Output device 16 Input device 19 Evaluation program 21 Acquisition unit 22 Disassembly unit 23 Calculation unit 24 Output processing unit

Claims (6)

A skin evaluation method performed by a computer equipped with at least a CPU (Central Processing Unit) and memory.
The process of acquiring the unevenness information of the skin surface measured from the skin or skin replica,
A decomposition step of multiplying the acquired unevenness information to extract unevenness information of a plurality of spatial frequency bands different from each other, and a decomposition step.
A calculation step of calculating a plurality of types of statistical values related to the uneven shape, which are different types for each spatial frequency band, from the unevenness information of any one of the plurality of spatial frequency bands that have been multiple-decomposed.
An output process that outputs the calculated plurality of types of statistical values for the evaluation of the skin, and
Skin evaluation method including. Wherein the plurality of types of statistical values output by said output step includes a pre-Symbol skewness and kurtosis calculated for each spatial frequency band different from each other in the calculating step at least,
The skin evaluation method according to claim 1. A multiple regression equation obtained in advance by multiple regression analysis using the skin sample data of the mother group, with a multiple regression equation for the purpose variable smoothness degree of the skin and explanatory variables the skewness and the kurtosis The process of determining the degree of smoothness of the skin,
The skin evaluation method according to claim 2, further comprising. An acquisition method for acquiring information on the unevenness of the skin surface measured from the skin or a replica of the skin,
Decomposition means that multiplexes the acquired unevenness information and extracts unevenness information of a plurality of spatial frequency bands that are different from each other.
A calculation means for calculating a plurality of types of statistical values related to the uneven shape, which are different types for each spatial frequency band, from the unevenness information of any of the plurality of spatial frequency bands that have been multiple-decomposed.
An output processing means for outputting the skin evaluation information using the calculated plurality of types of statistical values, and
A skin evaluation device equipped with. The calculation means calculates at least the skewness and kurtosis of the uneven shape as the plurality of types of statistical values.
The output processing means generates the evaluation information of the skin using at least the skewness and the kurtosis calculated for each spatial frequency band different from each other by the calculation means.
The skin evaluation device according to claim 4. The output processing means is a multiple regression equation previously acquired by multiple regression analysis using population skin sample data, with the skewness and kurtosis as explanatory variables and the smoothness of the skin as the objective variable. The degree of smoothness of the skin is determined by using the multiple regression equation.
The skin evaluation device according to claim 5.

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