Method for classifying the colors of makeup products and tool for supporting the development of the colors of makeup products

Abstract

A computer-implemented tool, intended to be controlled by a user to assist in the development of the color of a makeup product, includes a mapping mode adapted such that the user selects a group of colors of a makeup product from a makeup product color bank, and the mapping mode is configured to classify each color of the selected group of colors according to a method for classifying the color of a makeup product according to labels designed according to the subjective perception of makeup colors. The mapping mode is configured to display a map of the selected colors, arranged in a table according to their respective assigned labels, the table being organized by major rows for each color system group label and major columns for each hue sub-system group label, each major row including sub-rows for each lightness sub-system group label, and each major column including sub-columns for each saturation sub-system group label.

Description

Technical Field

[0001] Embodiments of the present invention relate to a method for classifying the colors of cosmetic makeup products, which is automatically implemented by a computer, and a tool for assisting in the development of the colors of cosmetic makeup products, which is implemented by a computer and controlled by a user.

Background Art

[0002] "Cosmetic products" means any product as defined in Regulation (EC) No. 1223 / 2009 of the European Parliament and of the Council of 30 November 2009 on cosmetic products. Cosmetic makeup products or "makeup products" more specifically are intended to cover the body surface in order to correct the perceived color and / or texture.

[0003] The development of the colors of makeup products involves visualizing and comparing the colors of makeup products existing in a given market for different skin tones, for example, to identify the preferred color trends of customers or to identify the color combinations that are missing and may be needed in a given market.

[0004] For example, the markets surveyed can vary from low-cost products to premium products, and the products can vary depending on the location of the market, such as the European market being different from different Asian or Chinese markets.

[0005] The comparison between makeup products can be carried out between different brands and franchises of the same company or between the owned legacy products and competing products.

[0006] Visualization is typically done by color experts using physical samples of color, such as thumbnail color samples of makeup products, under human vision, due to large responses in the subjective perception of makeup colors that can be generated by slight changes in absolute color. In fact, in the example of the red of a lipstick, a first red may appear subjectively cold and dull, while a second red that is absolutely very close to the first red may appear subjectively warm and rich.

[0007] The absolute difference between two colors can be, for example, the distance separating these colors in a "standard observer" model such as the "CIEXYZ" color space or the "CIELAB" color space. These standard observer models are defined by the International Commission on Illumination (abbreviated as CIE), and the colors they define are not related to any specific device such as a computer monitor or printer, but are related to the CIE standard observer, which is the average of the results of color matching experiments under laboratory conditions.

[0008] The RGB "red-green-blue" color space is defined by the coordinates of the additive primary colors of red, green, and blue and is commonly used in electronic devices for detecting and displaying colors.

[0009] The "CIELAB" color space, also known as L*a*b*, represents color as three coordinates. Let the lightness be L*, the chromaticity from greenish to reddish be a*, and the chromaticity from bluish to yellowish be b* (chromaticity represents a scale of both hue and saturation). In the a*b* plane, a unique hue can be identified by a unique angle within the triangular circle. The CIELAB color space is designed to be more perceptually uniform than, for example, the RGB color space. However, the subjective perception of the colors of makeup products is not well translated in the CIELAB color space, and as a result, slight changes in absolute color in the CIELAB color space can still result in large responses in the subjective perception of makeup colors.

[0010] The perception of human eye color goes beyond L*a*b* coordinates, making subjective perceptions such as the "background effect" of color important. For example, when looking at a single-color square surrounded by a colored background, the perception of the color of the square varies depending on the color of the background. Regarding makeup products, the perception of the color of lipstick can vary depending on the skin tone of the user.

[0011] As a result, providing a computerized tool to assist color experts in the development of makeup product colors is difficult due to discrepancies in the digital transfer of colors such as RGB or L*a*b* coordinates regarding the subjective perception of makeup colors.

[0012] Furthermore, another issue encountered in computerized tools for assisting color experts in the development of makeup product colors is the fact that computer monitors cannot display the same range of colors equally, and the range of colors that can be displayed (referred to as the "number of colors") is limited in both cases. The resulting problem is that two different colors with distinguishable characteristics under the perception of the human eye may be displayed exactly the same on various monitors and thus may not be usable for analysis by makeup color experts.

[0013] Therefore, the visualization and comparison of makeup product colors are still conventionally carried out under laboratory conditions using a very large number of thumbnail color samples of makeup products in many cases and cannot be remotely performed using a computer, for example, under home or office conditions.

[0014] Another issue in the development of makeup product colors is the fact that the color applied to the product can vary depending on the skin tone of the user who uses the makeup, and the subjective perception of color can also vary depending on the skin tone of the user. For example, as described above, the "background effect" of skin tone can change the perception of the color of makeup products. Basically, light or pale makeup colors become darker when applied to dark skin, and dark and rich makeup colors may appear overly strong when applied to fair skin and may appear to match dark skin exactly.

[0015] Therefore, the development of the color of makeup products has conventionally required a trial stage for human models in order to analyze the actual subjective perception when makeup is used. These trial stages are typically time-consuming and expensive. Summary of the Invention Problems to be Solved by the Invention

[0016] Therefore, there is a need to provide a tool, particularly a computerized tool, to assist color experts in the development of the color of makeup products and save time, which is adapted with respect to the subjective perception of makeup colors and is not limited to the performance of a computer monitor such as "color number", and provides the ability to develop appropriate makeup colors across various skin tones. Means for Solving the Problems

[0017] According to an aspect of the present invention, a computer-implemented method for automatically classifying the color of a makeup product comprises - providing input coordinate data in the L*a*b* space of the color of the makeup product, - assigning a color family group label (e.g., among brown, pink, orange, purple, red) to the color of the makeup product according to the identification of the color family group volume among a set of color family group volumes in the L*a*b* space containing the input coordinate data and the color family group volume is designed according to the subjective perception of makeup colors, and the color of the makeup product is classified according to the assigned label.

[0018] The subjective perception of the makeup colors used in the volume design, as well as the boundary values, lines, and surfaces defined below, are advantageously defined by human color experts, for example, according to the aforementioned susceptible subjective perception specific to the makeup colors. Advantageously, a human color expert can provide a visual database of discrete color points and perform the design of the volume and boundary values, lines, and surfaces in order to obtain the continuity of the color points by means of data-driven calculations configured to conform to the visual database. For example, the data-driven calculations configured to conform to the visual database can be implemented by a machine learning training model.

[0019] In other words, the computer-implemented method according to this aspect enables color classification based on labels representing the subjective perception of makeup colors and is defined by conditions established by the subjective perception inherent in the makeup colors. Thus, for example, the resulting classification applied to each color in the analyzed group of makeup products can provide useful information to color experts developing the colors of makeup products, despite the limitations in the computer representation of the color (color space) and the display of the color on the screen.

[0020] According to an embodiment, a set of color family volume in the L*a*b* space is a database containing a finite number of points in the L*a*b* space, each point being labeled with its respective family group label according to the subjective perception of the makeup color, and a mathematical calculation that includes triangulation to generate an envelope enclosing all the points of each respective family group and interpolation to expand the envelope until the opposing surfaces of adjacent envelopes coincide with each other, and the envelope defines the enclosure of each color family volume.

[0021] According to an embodiment, the color family volume is configured to demarcate the colors of brown, pink, orange, purple, and red in the L*a*b* space according to the subjective perception of the makeup color.

[0022] According to an embodiment, the method is - In accordance with the identification of the position of the input coordinate data, assign a lightness sub-system group label (e.g., one of light, medium, dark) to the color of the makeup product by comparing it with at least one lightness boundary value on the lightness coordinate axis L* of the L*a*b* space. Additionally include that at least one lightness boundary value is designed according to the subjective perception of the makeup color, and the color of the makeup product is classified according to the assigned label.

[0023] According to an embodiment, at least one lightness boundary value on the lightness coordinate axis L* of the L*a*b* space decreases in steps for input coordinates having a chroma value greater than a threshold value set according to the subjective perception of the makeup color.

[0024] According to an embodiment, the lightness sub-system group includes a light label assigned when the lightness input coordinate is greater than a first lightness boundary value, a medium label assigned when the lightness input coordinate is between the first lightness boundary value and the second lightness boundary value, and a dark label assigned when the lightness input coordinate is lower than the second lightness boundary value.

[0025] According to an embodiment, the method is - In accordance with the position of the input coordinate data, assign a chroma sub-system group label (e.g., one of high, medium, low) to the color of the makeup product by comparing it with at least one chroma boundary line in the a*b* plane of the L*a*b* space. Additionally include that at least one chroma boundary line is designed according to the subjective perception of the makeup color, and the color of the makeup product is classified according to the assigned label.

[0026] According to an embodiment, at least one chroma boundary line in the a*b* plane of the L*a*b* space changes according to the hue of the input coordinate data in accordance with the subjective perception of the makeup color.

[0027] According to an embodiment, the saturation lower system group includes a high label assigned when the saturation of the input coordinates is greater than the first saturation boundary line, an intermediate label assigned when the saturation of the input coordinates is between the first saturation boundary line and the second saturation boundary line, and a low label assigned when the saturation of the input coordinates is lower than the second saturation boundary line.

[0028] According to an embodiment, the method - Assigning a hue lower system group label (e.g., among cool colors, neutral colors, and warm colors) to the color of the makeup product by comparing it with at least one hue boundary surface inside each color system group volume in the L*a*b* space according to the position of the input coordinate data is additionally included, and at least one hue boundary surface is designed according to the subjective perception of the makeup color, and the color of the makeup product is classified according to the assigned label.

[0029] According to an embodiment, at least one hue boundary surface of each color system group volume in the L*a*b* space is determined according to the subjective perception of the makeup color.

[0030] According to an embodiment, the hue lower system group includes a warm color label assigned when the input coordinates are located on one side of the first hue boundary surface, a neutral color label assigned when the input coordinates are located between the other side of the first hue boundary surface and one side of the second hue boundary surface, and a cool color label assigned when the input coordinates are located on the other side of the second hue boundary surface.

[0031] According to an embodiment, the color of the makeup product is hierarchically classified first according to the color system group label, second according to the hue lower system group label, and then according to the lightness lower system group label and the saturation lower system group label.

[0032] This hierarchy enables, for example, convenient classification of labels for color analysis of makeup products. That is, other hierarchies can also be used, and regarding the process for assigning labels, since the lightness boundary value may depend on the chroma label, can all the respective processing steps be executed simultaneously, or in some cases, the lightness label may need to be processed after the chroma label.

[0033] According to another aspect, a computer-implemented tool intended to be controlled by a user for assisting in the development of makeup product colors is - A mapping mode adapted to enable a user to select a group of makeup product colors from a makeup product color bank, configured to classify each color in the selected group of colors using a method for classifying the makeup product colors defined above, and to display a map of the selected colors arranged in a table according to the respectively assigned labels, the table being organized by major rows for each color family group label and major columns for each hue tone subgroup label, each major row including sub-rows for each lightness subgroup label, and each major column including sub-columns for each chroma subgroup label, the mapping mode including.

[0034] According to an embodiment, the table is - Five major rows for each of the color family group labels of brown, pink, orange, purple, and red respectively, - Three major columns for each of the hue tone subgroup labels of cool, neutral, and warm respectively, - Three sub-rows for each major row for each of the lightness subgroup labels of light, medium, and dark respectively, and - Three sub-columns for each major column for each of the chroma subgroup labels of low, medium, and high respectively organized by.

[0035] According to an embodiment, the tool is - An application mode adapted for a user to select a set of colors of at least one makeup product from a displayed map (ID card) and to select at least one skin tone photo model, and configured to display an image of a simulation of the application of the selected set of colors of the makeup product on at least one skin tone photo model further includes.

[0036] According to an embodiment, the tool - A color creation mode adapted for a user to select at least one skin tone photo model and to set parameters for generating a custom color, and configured to display an image of a simulation of the application of the custom color on at least one skin tone photo model further includes.

[0037] According to an embodiment, the color creation mode is further adapted for a user to select a set of colors of at least one makeup product from a displayed map (ID card), and configured to simultaneously display a comparison image of the application of the custom color and the selected set of colors of the makeup product on at least one skin tone photo model respectively.

[0038] According to another aspect, a computer program product includes instructions that, when executed by a computer, cause the computer to perform the method defined above or cause the computer to perform the tool defined above.

[0039] According to another aspect, a computer-readable storage medium includes instructions that, when executed by a computer, cause the computer to perform the method defined above or cause the computer to perform the tool defined above.

[0040] In other words, the aspects and embodiments particularly provide a digital system that enables real-time analysis of the shade of color cosmetics and creation of a shade range based on an integration process of functions, such as: 1) data visualization of device measurement data by a label adapted to the classification of the color of makeup products; 2) digital application of the measured shade in an image showing models of various skin tones obtained, for example, using a pseudo-spectral imaging system; 3) digital evocation and accurate application of color tones to the obtained images, for example, using the aforementioned pseudo-spectral camera system.

[0041] Therefore, a method and system (tool) are defined that enable integration of the development of a shade range, particularly considering the evaluation of makeup in various skin tones, for a makeup product development team using a digital platform.

[0042] Thereby, the product development team can easily create a shade range by providing an integrated ability to visualize the shade from a database of measured shades and digitally evoke new shades, enabling the creation of a higher-performing shade range for color cosmetics that matches the skin tone.

[0043] Furthermore, this increases the efficiency of shade range development and can shorten the shade range development time by approximately 25% because, in some cases, it enables digital visualization and comparison of lipstick shades in dedicated classifications and simulations on the face.

[0044] Furthermore, the aspects and embodiments defined above enable the development of a shade range that is considered appropriate across a variety of skin tones. This can reduce the number of years in which shades are created because the development team can visualize and select shades from the database to match a specific skin tone.

[0045] Other advantages and specifications of the present invention will become apparent in the examination of the detailed description of the embodiments, without limitation, in relation to the accompanying drawings.

Brief Description of the Drawings

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Modes for Carrying Out the Invention

[0047] Figure 1 shows a method 100 for classifying the colors of a makeup product 102, designed to be automatically implemented by a computer. Preferably, the makeup product 102 is a lipstick product. Here, a method for providing a classification label for a single color 104 of a single makeup product 102 will be described. For example, in step 102, the makeup product 102 is selected from among a database of makeup product colors. However, it will be understood that the classification is intended to be applied to some colors of some makeup products, mainly to distinguish these colors according to the subjective human perception of makeup colors. In such a case, the method is executed for each color of a set of some makeup products.

[0048] The method includes, in step 104, providing input coordinate data in the L*a*b* space of the color 102 of the makeup product. As described above and as will be described below in connection with FIG. 2, the L*a*b* space is the conventional "CIELAB" color space that represents color as three coordinates. Let the lightness be L*, the greenish chromaticity to the reddish chromaticity be a*, and the bluish chromaticity to the yellowish chromaticity be b* (chromaticity represents a scale of both hue and saturation). Step 104 may include a conventional conversion of the color coordinates from any other color space to the coordinates in the L*a*b* color space.

[0049] The first assignment step 106 assigns a color family group label (which may be, for example, a brown, pink, orange, purple, or red color family group) to the colors 102-104 of the makeup product. As will be disclosed below in connection with FIG. 3, the color family group label can be assigned according to the identification of a color family group volume within a set of color family group volumes in the L*a*b* space that includes the input coordinate data. In particular, the color family group volume is specifically designed according to the subjective perception of makeup colors. For example, as will be described below in connection with FIG. 3, the color family group volume is preferably determined by compatibility with a visual database according to data-driven calculations.

[0050] The second assignment step 108 assigns a lightness sub-group label (which can be, for example, a light, medium, or dark lightness sub-group), a chroma sub-group label (which can be, for example, a high, medium, or low chroma sub-group), and a hue-tone sub-group label (which can be, for example, a cool, neutral, or warm hue-tone sub-group) to the colors 102 - 104 of the makeup product.

[0051] As will be described below with reference to FIG. 4, the lightness sub-group label can be assigned according to the identification of the position of the input coordinate data 104 compared to at least one lightness boundary value on the lightness coordinate axis L* of the L*a*b* space. In particular, at least one lightness boundary value is specifically designed according to the subjective perception of the makeup color.

[0052] As will be described below with reference to FIG. 5, the chroma sub-group label can be assigned according to the position of the input coordinate data 104, compared to at least one chroma boundary line in the a*b* plane of the L*a*b* space. In particular, at least one chroma boundary line is specifically designed according to the subjective perception of the makeup color.

[0053] As will be described below with reference to FIG. 6, the hue-tone sub-group label can be assigned according to the position of the input coordinate data 104, compared to at least one hue-tone boundary surface inside each color group volume 106 in the L*a*b* space. In particular, at least one hue-tone boundary surface is specifically designed according to the subjective perception of the makeup color.

[0054] In the final step 110, the colors of the makeup product are classified according to the assigned labels, for example, advantageously in an identification information card map hierarchically arranged as will be described later with reference to FIG. 7.

[0055] FIG. 2 shows an exemplary discrete representation of the L*a*b* color space, also known as the CIELAB color space. The CIELAB color space represents color as three coordinates. The lightness is L*, the greenish hue to the reddish hue is a*, and the bluish hue to the yellowish hue is b*.

[0056] The L*a*b* space enables the easy derivation of the values of hue, chroma, and color point in the L*a*b* coordinate system. Since the hue of the color point is specified by the triangle angle θ in the a*b* plane containing that point, θ = tan -1 (b* / a*) can be represented. Since the chroma of the color point is defined by the distance from the origin (a* = 0, b* = 0) in the a*b* plane containing that point, C* = (a* 2 + b* 2 ) 1 / 2 can be represented. The value of the color point is determined by its lightness coordinate L*.

[0057] FIG. 3 shows the generation of a set of color gamut volumes in the L*a*b* space used for identifying the color gamut group labels of input coordinate data in the first assignment step 106 of method 100.

[0058] First, a database containing a finite number of points within the L*a*b* space for each color gamut group is provided, and each point is labeled with its respective color gamut group label BRN, ORG, PNK, RD, PRP as shown by scatter plot 302. In this step, the labels are "manually" assigned to each point in the database by a human color expert according to the subjective perception of makeup colors. These labels may correspond to the evaluation of the brown color gamut group BRN, the orange color gamut group ORG, the pink color gamut group PNK, the red color gamut group RD, and the purple color gamut group PRP. This "manual" assignment is performed once to configure a computer-implemented classification method according to a specific subjective perception of makeup colors. This one-time manual assignment can be performed according to the conventional techniques of makeup color classification described below in relation to FIG. 8.

[0059] Second, for each discrete color system group's scatter plot, mathematical calculations are performed to extrapolate a continuous volume within the L*a*b* space. As shown by plot 304, the mathematical calculations generate an envelope enclosing all the points of each respective system group, for example, by conventional triangulation techniques such as Delaunay triangulation and alpha shape generation.

[0060] Furthermore, the mathematical calculations advantageously perform interpolation to expand the envelope, as shown by plot 306. The expansion is configured to fill the gaps between the color system group volumes until the opposing surfaces of each adjacent envelope match each other. This can be performed, for example, by selecting the nearest neighbors according to the distance to the triangulation surface of each color system group.

[0061] The envelope obtained in plot 306 defines the enclosure of each color system group volume when all of the opposing surfaces of each adjacent envelope match each other without a gap therebetween.

[0062] Accordingly, the input points arranged within the L*a*b* color space according to their coordinates are assigned the respective labels BRN, ORG, PNK, RD, PRP of the envelope enclosing the input points within the L*a*b* color space.

[0063] FIG. 4 shows the identification of the positions of the input coordinate data 104 compared with at least one lightness boundary value BndVal1, BndVal2 used to assign lightness sub-system group labels in step 108 of method 100.

[0064] In this example, the lightness sub-group labels include the lightness label LGT of "light", the lightness label MDM of "medium", and the lightness label DRK of "dark", and are identified by comparing with the first lightness boundary value BndVal1 and the second lightness boundary value BndVal2 lower than the first lightness boundary value BndVal1. When the lightness input coordinate L* is greater than the first lightness boundary value BndVal1, the light label LGT is assigned; when the lightness input coordinate L* is between the first lightness boundary value BndVal1 and the second lightness boundary value BndVal2, the medium label MDM is assigned; when the lightness input coordinate L* is less than the second lightness boundary value BndVal2, the dark label DRK is assigned.

[0065] Here too, both lightness boundary values BndVal1 and BndVal2 are specifically designed according to the subjective perception of the makeup color. In particular, the levels on the L* axis of the lightness boundary values can be set according to the chroma of the input coordinate data.

[0066] In addition, the lightness boundary values BndVal1 and BndVal2 preferably decrease in step C*Stp for input coordinates having a chroma value C* greater than the threshold values selected according to the subjective perception of the makeup color. In fact, the lightness boundary values BndVal1 and BndVal2 slightly decrease for the "high" chroma sub-group labels assigned in step 108 of method 100, particularly in relation to FIG. 5. This preferably makes it possible to compensate for the effect that the subjective perception of lightness increases with chroma, which is called the Helmholtz-Kohlrausch effect.

[0067] FIG. 5 shows the identification of the position of the input coordinate data 104 compared with at least one chroma boundary line BndCrcl1, BndCrcl2 in the a*b* plane including input coordinate points used to assign chroma sub-group labels in step 108 of method 100.

[0068] In this example, the chroma sub-system group labels include the high chroma label HGH, the medium chroma label INTR, and the low chroma label LW, and are identified in comparison with the first chroma boundary line BndCrcl1 and the second chroma boundary line BndCrcl2 that is lower than the first chroma boundary line BndCrcl1. In any a*b* plane, the chroma level C* is the Euclidean distance from the origin (0,0), that is,

Number

[0069] When the chroma of the input coordinate C* is greater than the first chroma boundary line BndCrcl1, the high label HGH is assigned; when the chroma of the input coordinate C* is between the first chroma boundary line BndCrcl1 and the second chroma boundary line BndCrcl2, the intermediate label INTR is assigned; when the chroma of the input coordinate C* is lower than the second chroma boundary line BndCrcl2, the low label LW is assigned.

[0070] Here too, both of the chroma boundary lines BndCrcl1 and BndCrcl2 are specifically designed according to the subjective perception of the makeup color. In particular, these lines are defined only for the hues in the a*b* plane that are susceptible to the influence of the application of makeup products. In the example of a lipstick makeup product, the hues susceptible to the influence of the application are approximately located from the semi-plane of positive values of a*, that is, the yellowish orange hue yORG to the bluish purple hue bPRP.

[0071] Furthermore, the chroma boundary lines BndCrcl1 and BndCrcl2 advantageously depend on the hue of the input coordinate data in order to take into account the subjective perception of chroma according to the hue of each color. In fact, for example, the orange hue yORG appears to be "weaker" with respect to chroma than the purple hue bPRP. As a result, both chroma boundary lines BndCrcl1 and BndCrcl2 have a slightly larger radius at positive values on the b* side (the yellow-orange hue yORG) compared to the spiral radius around the 0 value of b* (the reddish hue at positive values of a*), and may have the appearance of part of a spiral with a slightly narrower radius at negative values on the b* side (the blue-purple hue bPRP).

[0072] FIG. 6 shows the identification of the position of the input coordinate data 104 compared to at least one hue boundary surface BndSrfc1, BndSrfc2 in the L*a*b* space, more specifically the color system group volume (FIG. 3) containing the input coordinate points, which is used to assign the hue subgroup labels in step 108 of method 100.

[0073] In this example, the hue subgroup labels include a "warm color" hue label WRM, a "neutral color" hue label NTR, and a "cool color" hue label CL, and are identified by comparison with a first hue boundary surface BndSrfc1 and a second hue boundary surface BndSrfc2, delimiting the space inside each color system group volume BRN, ORG, RD, PNK, PRP.

[0074] A warm color label WRM is assigned when the input point is located on one side of the first hue boundary surface BndSrfc1, a neutral color label NTR is assigned when the input point is located between the other side of the first hue boundary surface BndSrfc1 and one side of the second hue boundary surface BndSrfc2, and a cool color label CL is assigned when the input point is located on the other side of the second hue boundary surface BndSrfc2.

[0075] Here too, both hue-tone boundary surfaces BndSrfc1 and BndSrfc2 are specifically designed according to the subjective perception of the makeup color. In particular, the positions of these surfaces and the range of neutral colors are determined by the visual results of makeup color experts. The hue-tone gradation boundary surfaces BndSrfc1 and BndSrfc2 are simultaneously defined along the lightness (e.g., L* = 20 to 90) and chroma (e.g., C* = 0 to the highest level), and their coordinate definitions can be registered in a look-up table for identifying the relative positions of the input coordinate data.

[0076] Figure 7 shows an exemplary result in the final step 110 of the classification method 100 described above in relation to FIGS. 1 to 6.

[0077] In the final step 110, the colors of the makeup products are advantageously classified according to the assigned labels, illustrated by the identification card map shown, which shows a hierarchical arrangement of colors by, firstly, color family group labels, secondly, hue-tone subgroup labels, and then, similarly, lightness subgroup labels and chroma subgroup labels.

[0078] In the illustrated identification information card map, multiple-color makeup products are classified together, and each color is processed by the method described above in relation to FIGS. 1 to 6 for the assignment of respective labels that define the position of the color in the identification information card map.

[0079] Accordingly, in the identification information card map, the colors are arranged in a table organized by the main rows of each color family group label of "brown", "pink", "orange", "purple" and "red" and the main columns of each hue-tone subgroup label of "cool color", "neutral color" and "warm color". Further, each main row includes a set of sub-rows for each lightness subgroup label of "light", "medium" and "dark", and each main column includes a set of sub-columns for each chroma subgroup label of "low", "medium" and "high".

[0080] Instead, the table is compiled without the set of lower columns of each chroma sub-group label, and the colors within each lightness row can be replaced by sorting the colors in ascending order of their chroma values, for example, from left to right within each row.

[0081] Since the colors are arranged according to the labels in this identification information card map, that is, according to the subjective perception that occurs as a makeup product, color experts can perform an improved analysis of the classified groups of makeup colors.

[0082] In fact, first, since the classification is automatically performed by a computer, the identification information card map can be generated very quickly and many times for different selections of groups of colors of makeup products.

[0083] Therefore, this method makes it possible to save a significant amount of time for color experts compared to the prior art for this type of classification performed manually by visual inspection of thumbnail color samples as shown in FIG. 8.

[0084] Second, due to the positions of the colors corresponding to the subjective perception caused as a makeup product, the identification information card map is not restricted by the color display performance of a given computer monitor. In fact, a computer monitor can display the same color for two different data. For example, a low-chroma warm-pink can be displayed as identical to a low-chroma cool-brown on a computer monitor because its performance is restricted in terms of the "number of colors" compared to the sensitivity of human eye perception. Such a situation would lead to an incorrect analysis of these colors without the classification in the identification information card map. However, the positions of the two colors in the identification information card map provide information about their actual differences to color experts, even though these two colors are displayed by the same signal of the computer monitor.

[0085] FIG. 8 is a photograph of a prior art for classifying the colors of makeup products in order to compare, for example, products of different brands and franchises of the same company or legacy products owned and competing products.

[0086] Conventionally, the classification was performed by a color expert using physical samples of colors under the perception of the human eye, for example, characterized individually according to subjective perception, using thumbnail color samples of makeup products spread on an experimental table or a whiteboard.

[0087] This prior art is clearly time-consuming and not adapted or even impossible to be executed multiple times for many different groups of makeup colors. On the other hand, as described above, the identification information card map can be easily generated immediately as many times as necessary.

[0088] FIG. 9 shows an example of a conventional computer representation of a group of colors, here a plot projected onto the a*b* two-dimensional plane (i.e., by ignoring the L* coordinate). Even though the a*b* plane of CIELAB was designed to present a representation approximating human color perception, such a representation cannot be used to analyze makeup colors. In fact, even if it is possible for a color expert to partially visually perform the identification of color families such as "brown," "orange," "pink," "red," "purple" for such a plot, other characterizations are limited to the execution of the color display on the computer screen.

[0089] The aforementioned problem remains that the same color signal is displayed on the computer monitor for two different color data (e.g., a low-chroma warm pink can be displayed in the same way as a low-chroma cool brown), and the classification of makeup colors by subjective perception is not practical.

[0090] From another perspective, the chroma value can be regarded as the distance of the color point from the origin. However, the subjective perception of chroma varies according to the hue. As a result, for a given hue (e.g., the purple hue), a high-chroma point may be plotted closer to the origin than a lower-chroma point in another hue (e.g., the orange hue), and as a result, the color points may be mixed in the a*b* plot and may appear unsystematic. Classification based on the subjective perception of makeup colors must remain visual and is thus not practical due to the limitations of computer monitor display performance.

[0091] FIG. 10 shows a complete computer tool for assisting a color expert, i.e., a "user," in the development of makeup product colors that benefits from the classification method and identification information card map described above in relation to FIGS. 1-7.

[0092] A tool implemented by a computer controlled by a user may be actually embodied as a computer program product that includes instructions for causing the computer to implement the tool according to the present disclosure when the program is executed by the computer, or may be actually embodied as a computer-readable storage medium that includes instructions for causing the computer to implement the tool according to the present disclosure when executed by the computer.

[0093] The tool first includes a mapping mode configured to provide an identification information card map as described above in relation to FIG. 7 for a selected group of makeup colors.

[0094] Thus, the user can select a group of makeup product colors from a makeup product color bank, such as a bank that includes in-house brand and franchise makeup colors and / or competing company brand and franchise makeup colors, as displayed in the mapping mode.

[0095] The user can also select the market for which the makeup product is targeted or many other options according to the business department of the color bank.

[0096] Thus, in the example of FIG. 7, the identification information card map classifies the selected color groups in a table composed of five main rows for the color family group labels of brown, pink, orange, purple, and red respectively, three main columns for the hue sub-group labels of cool, neutral, and warm colors respectively, three sub-rows for each main row for the lightness sub-group labels of bright, medium, and dark respectively, and three sub-columns for each main column for the saturation sub-group labels of low, medium, and high respectively.

[0097] Thus, the mapping mode of the tool provides the user with data visualization of the instrumental measurement data of a table arranged according to the subjective perception of makeup colors. Through mapping, the user can select the color range of successful competitors for visualization in this meaningful identification information card map, for example, compared with the color range within the company, and understand the blank space that the in-house brand does not currently cover with the current color.

[0098] FIGS. 11, 12, and 13 show the application mode of a tool adapted to digitally simulate the visualization on the face of the color range applied for more specific considerations of the makeup colors selected by the user.

[0099] The application mode is adapted such that the user selects a set of colors of one or more makeup products from the identification information card map displayed in the mapping mode and selects one skin tone photo model or several skin tone photo models. The application mode is configured to display an image of the simulation of the application of the selected set of colors of the makeup product on at least one skin tone photo model respectively.

[0100] Advantageously, the simulation of the application of each set of colors of at least one makeup product on a selected skin tone photographic model is performed by the pseudo-spectral imaging system disclosed in the scientific publication "Liu Z., Xiao K., Pointer M. et al., 'Developing a multi-spectral imaging system using a RGB camera under two illuminations: Proceedings of the 28th IS&T Color and Imaging conference. 28th Color and Imaging Conference, 04 - 19 Nov 2020, Online. Society for Imaging Science and Technology'"; or the technique disclosed in the US patented document US Patent No. 8,498,456 (titled "Method and system for applying cosmetic and / or accessoral enhancements to digital images"); or the technique disclosed in the US patented document US Patent Application Publication No. 2018 / 0075524 ("Applying virtual makeup products").

[0101] Figure 11 shows the options possible in the application mode. In this example, the user selected approximately nine or more shades from the mapping mode and the user selected one skin tone photographic model.

[0102] The image of the simulation of applying the selected set of makeup product shades to the selected skin tone photographic model is displayed on a grid that dynamically adapts to the selection. In this example, the grid is a 3×3 image and is scrollable up and down to browse the grid.

[0103] To display the simulation image, two view options may be provided, such as a full-face view (see Figure 12) or a close-up view as shown in Figure 11, for example.

[0104] The color selection can be changed at any time by the same process as the mapping mode, for example, using the checkboxes for each image of the simulation. It is also possible to automatically reflect the changed color selection in the mapping mode.

[0105] Figure 12 shows other possible options in the application mode. In this example, the user selects two color combinations from the mapping mode or a previous application mode, for example, from the example of the application mode in Figure 11, and the user selects two skin tone photo models.

[0106] Figure 13 shows other possible options in the application mode. In this example, the user selects two color combinations from the mapping mode or a previous application mode, for example, from the example of the application mode in Figure 11, and the user selects one skin tone photo model. When only two color combinations are selected, the application mode provides a parallel comparison mode, and by dragging the cursor left and right, the two color combinations can be compared immediately.

[0107] Figure 14 shows the color creation mode of a tool adapted to digitally create a new color combination and simulate the visualization of the color being created on the face as a preliminary design step for creating a new makeup product.

[0108] The color creation mode is adapted such that the user selects at least one skin tone photo model and sets parameters to generate a custom color. The color creation mode is configured to display in real time an image of the simulation of the application of the custom color on at least one skin tone photo model.

[0109] For example, the parameters for generating a custom color may include a hue parameter, a saturation parameter, and a lightness parameter. Finish parameters such as "matte", "satin", or "gloss" may also be provided.

[0110] Furthermore, the color creation mode is advantageously adapted so that the user can select the color of one existing makeup product from the displayed identification information card map, and is configured to simultaneously display on at least one skin tone photographic model a comparison image of the simulation of the application of the set of the custom color and the color of the selected makeup product. For example, the simultaneous display of the comparison images can be implemented with a drag cursor as shown in FIG. 13 or a grid of simulated images as shown in FIGS. 11 or 12.

[0111] For example, the user can further compare the display of the color difference Δ(E), L*, C*, h(θ) with the two color combinations by viewing the two color combinations digitally applied to the face when viewed side by side.

[0112] When resumed, the tools described above in connection with FIGS. 10 to 14 provide a digital system that enables real-time analysis of the creation of the color and range of colors of color cosmetics based on an integrated process that is interacted with by the user according to the following. 1) Data visualization representing a specific subjective perception of makeup colors, enabling the user to select color combinations for visualizing and performing a meaningful analysis of products and markets. 2) Digital application of the measured color combinations of images depicting models of different skin tones obtained using a pseudo-spectral imaging system, enabling the user to visually evaluate the color combinations of models with different skin tones by digitally applying a color combination (e.g., the color combination of a lipstick) to an image showing the face of a model with a different skin tone, and to understand the suitability for the market. 3) A function that enables a user to digitally create a color scheme, accurately envision, and apply it to an image obtained using the above-described pseudo-spectral simulation imaging system, by using the input of lightness, chroma, hue, and makeup finish values that are rendered in real time on the image of the model's face.

Claims

1. A computer-implemented method (100) for automatically classifying the colors of makeup products (102), comprising: - providing input coordinate data to the L*a*b* space (104) of the color of the makeup product; - assigning a color family group label (106) to the color of the makeup product according to the identification of the color family group volume (ORG, PNK, RD, PRP, BRN) in the L*a*b* space including the input coordinate data; The method (100) being a computer-implemented method, wherein the color family group volumes (ORG, PNK, RD, PRP, BRN) are designed according to the subjective perception of makeup colors, and the colors of the makeup products are classified according to the assigned labels.

2. The set of color family group volumes (ORG, PNK, RD, PRP, BRN) in the L*a*b* space is a database (302) including a finite number of points in the L*a*b* space, each point being labeled with its respective family group label according to the subjective perception of makeup colors, and a mathematical calculation including triangulation (304) for generating an envelope enclosing all the points of each respective family group, and interpolation (306) for expanding the envelope until the opposing surfaces of adjacent envelopes coincide with each other, the envelope defining the enclosure of the respective color family group volume, according to the method of claim 1.

3. The method according to claim 1 or 2, wherein the color family group volumes (BRN, PNK, ORG, PRP, RD) are configured to delimit the colors of brown, pink, orange, purple and red in the L*a*b* space according to the subjective perception of makeup colors.

4. - additionally assigning a lightness sub-family group label (108) to the color of the makeup product by comparing with at least one lightness boundary value (BndVal1, BndVal2) on the lightness coordinate axis L* of the L*a*b* space according to the identification of the position of the input coordinate data; The method according to any one of claims 1 to 3, wherein the at least one lightness boundary value (BndVal1, BndVal2) is designed according to the subjective perception of makeup colors, and the colors of the makeup products are classified according to the assigned labels.

5. The method according to claim 4, wherein the at least one lightness boundary value (BndVal1, BndVal2) on the lightness axis L* of the L*a*b* space decreases in a step (C*Stp) for input coordinates having a chroma value greater than a threshold set according to the subjective perception of the makeup color.

6. The lightness sub-system group includes a light label (LGT) assigned when the lightness input coordinate is greater than a first lightness boundary value (BndVal1), a middle label (MDM) assigned when the lightness input coordinate is between the first lightness boundary value (BndVal1) and a second lightness boundary value (BndVal2), and a dark label (DRK) assigned when the lightness input coordinate is lower than the second lightness boundary value (BndVal2). The method according to claim 4 or 5.

7. - Assigning a chroma sub-system group label (108) to the color of the makeup product by comparing with at least one chroma boundary line (BndCrc1, BndCrc2) in the a*b* plane of the L*a*b* space according to the position of the input coordinate data The method according to any one of claims 1 to 6, further comprising, wherein the at least one chroma boundary line (BndCrc1, BndCrc2) is designed according to the subjective perception of the makeup color, and the color of the makeup product is classified according to the assigned label.

8. The method according to claim 7, wherein the at least one chroma boundary line (BndCrc1, BndCrc2) in the a*b* plane of the L*a*b* space varies according to the hue of the input coordinate data according to the subjective perception of the makeup color.

9. The chroma sub-system group includes a high label (HGH) assigned when the chroma of the input coordinate is greater than a first chroma boundary line (BndCrc1), an intermediate label (INTR) assigned when the chroma of the input coordinate is between the first chroma boundary line (BndCrc1) and a second chroma boundary line (BndCrc2), and a low label (LW) assigned when the chroma of the input coordinate is lower than the second chroma boundary line (BndCrc2). The method according to claim 7 or 8.

10. - Assign a hue sub-group label (108) to the color of the makeup product according to the position of the input coordinate data, by comparing with at least one hue boundary surface (BndSrf c1, BndSrf c2) inside each color group volume in the L*a*b* space. The method according to any one of claims 1 to 9, additionally including that the at least one hue boundary surface (BndSrf c1, BndSrf c2) is designed according to the subjective perception of makeup colors, and the color of the makeup product is classified according to the assigned label. **Claim 11** The method according to claim 10, wherein the at least one hue boundary surface (BndSrf c1, BndSrf c2) of each color group volume in the L*a*b* space is determined according to the subjective perception of makeup colors. **Claim 12** The hue sub-group includes a warm color label (WRM) assigned when the input coordinates are located on one side of the first hue boundary surface (BndSrf c1), a neutral color label (NTR) assigned when the input coordinates are located between the other side of the first hue boundary surface (BndSrf c1) and one side of the second hue boundary surface (BndSrf c2), and a cool color label (CL) assigned when the input coordinates are located on the other side of the second hue boundary surface (BndSrf c2). The method according to claim 10 or 11. **Claim 13** The method according to any one of claims 1 to 12, combined with claims 4, 7 and 10, wherein the color of the makeup product is hierarchically classified first according to the color group label, second according to the hue sub-group label, and then according to the lightness sub-group label and the chroma sub-group label. **Claim 14** A computer-implemented tool intended to be controlled by a user for assisting in the development of the color of a makeup product, - A mapping mode (FIG. 10) adapted to allow the user to select a group of makeup product colors from a makeup product color bank, configured to classify each color in the selected group of colors using the method for classifying the colors of the makeup product according to any one of claims 1 to 13, and configured to display a map of the selected colors arranged in a table according to the respective assigned labels, the table being compiled by main rows for each color system group label and optionally main columns for each hue sub-system group label, each main row optionally including sub-rows for each lightness sub-system group label, and each main column optionally including sub-columns for each saturation sub-system group label, the mapping mode including a computer-implemented tool. **Claim 15** The method for classifying the colors of the makeup product is as described in any one of claims 1 to 13 in combination with claims 4, 7 and 10, and the table - Five main rows for each of the color system group labels of brown, pink, orange, purple and red respectively, - Three main columns for each of the hue sub-system group labels of cool, neutral and warm respectively, - Three sub-rows for each main row for each of the lightness sub-system group labels of light, medium and dark respectively, and - Three sub-columns for each main column for each of the saturation sub-system group labels of low, medium and high respectively compiling the tool according to claim 14. **Claim 16** - An application mode (FIGS. 11, 12, 13) adapted to allow the user to select at least one set of makeup product colors from the displayed map (ID card) and to select at least one skin tone photo model, configured to display an image of the simulation of the application of the selected set of makeup product colors on each of the at least one skin tone photo model, the application mode additionally included in the tool according to claim 14 or 15. **Claim 17** - A color creation mode (FIG. 14) adapted to allow the user to select at least one skin tone photo model and to set parameters for generating a custom color, configured to display an image of the simulation of the application of the custom color on the at least one skin tone photo model, the color creation mode The tool according to any one of claims 14 to 16, additionally including [

18. ] The color creation mode (FIG. 14) is additionally adapted such that the user selects a set of colors of at least one makeup product from the displayed map, and a comparison image of the simulation of the application of the custom color and the set of colors of the selected makeup product is simultaneously displayed on each of the at least one skin tone photographic model. The tool according to claim 17, configured as such. [

19. ] A computer program product including instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 13, or cause the computer to perform the tool according to any one of claims 14 to 18. [

20. ] A computer-readable storage medium including instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 13, or cause the computer to perform the tool according to any one of claims 14 to 18.

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