There is little research on the facial colour; for example, choice of cosmetics usually was focused on fashion or impulse purchasing. People never try to make right decision with facial colour. Meanwhile, facial colour can be also a method for health or disease prevention. This research puts forward one set of intelligent skin colour collection method based on human facial identification. Firstly, it adopts colour photos on the facial part and then implements facial position setting of the face in the image through FACE++ as the human facial identification result. Also, it finds out the human face collection skin colour point through facial features of the human face. The author created an SCE program to collect facial colour by each photo, and established a hypothesis that uses minima captured points assumption to calculate efficiently. Secondly, it implements assumption demonstration through the Taguchi method of quality improvement, which optimized six point skin acquisition point and uses average to calculate the representative skin colour on the facial part. It is completed through the Gaussian distribution standard difference and CIE 2000 colour difference formula and uses this related theory to construct the optimized program FaceRGB. This study can be popularized to cosmetics purchasing and expand to analysis of the facial group after big data are applied. The intelligent model can quickly and efficiently to capture skin colour; it will be the basic work for the future fashion application with big data.
Many studies on skin colour focus on face recognition or try to determine the typology of people [
There are many relevant software of screen collecting color, such as Just Color Picker, ColorPic, and ColorSPY to expert mapping software Photoshop; all have functions of collecting image and screen website color, merits of software such as Just Color Picker and ColorPic except to support color codes such as HTML, RGB, HEX, HSB/HSV, HSL, HSL(255), and HSL(240), even it provides simple palette tools, which can make us manually make the desired colors. Photoshop uses graphic expression to convey dye absorption and makes function of color filling, which indicates digitalized collected color and makes more users quickly get their desired referential color; although it is quick and convenient, it does not represent that the used software can precisely collect color; this is related to the used software in market which usually makes collection by pixel, which indicates that the chosen image area is not large-scale visual color seen by people, so choosing color does not mean the representative color of this image. Hsiao et al. put forward fuzzy relation matrix calculation program of the fuzzy method to implement; the aim lies in reducing color and converting image into color and chooses representative color of this area, which uses related concept of absorbing image color [
Soriano et al. put forward that skin color indicates different colors under different environments; scholars record skin color trace by using a digital camera and present color range of skin color by skin color space, establishment of skin color point will be affected by distance of human eyes seeing image skin color by establishing image skin color point, which indicates perception of human eyes is color of even skin color [
The purpose of this research mainly focuses on the facial skin colour; this is the extensive research based on human face identification, suppose image human face detection can use the minimum colour point as representative skin colour symbol, and through calculation of skin colour model and Taguchi method, it can minimize the colour point to 6 point and has representativeness. This application can make accumulation and calculation of plenty of data in the future, and it can be the base of big data analysis and expert system establishment for human skin colour. Figure
From Ellipsolid theory, make the SCE program to detect facial colour by each photo; Through the Taguchi method, get the 6-points captured method and make FaceRGB.
Since RGB colour models are device-dependent, there is no simple formula for conversion between RGB values and
It is followed by a matrix multiplication of the linear values to get
These gamma-corrected values are in the range 0 to 1. If values in the range 0 to 255 are required, the values are usually clipped to the 0 to 1 range. This clipping can be done before or after this gamma calculation [
The Taguchi method is used to make the designed product to have stable quality and small fluctuation and makes the production process insensitive to every kind of noise. In the product design process, it uses relations of quality, cost, and profit to develop high-quality product under condition of low cost. The Taguchi method thinks the profit of product development can use internal profit of enterprise and social loss to measure, enterprise internal profit indicates low cost under condition with the same functions, and social profit uses effect on human after product entering consumption field as the measurement index. This research uses the Taguchi method, and its main aim is to find out the optimization of skin colour point because point distribution has many probabilities, and it can find out the optimal point model through calculation of the Taguchi method.
Taguchi’s designs aimed to allow greater understanding of variation than a lot of the traditional designs from the analysis of variance. Taguchi contended that conventional sampling is inadequate here as there is no way of obtaining a random sample of future conditions. In Fisher’s design of experiments and analysis of variance, experiments aim to reduce the influence of nuisance factors to allow comparisons of the mean treatment effects [
Zeng and Luo conducted the studies in human skin colour luminance dependence cluster shape discussed in the Lab colour space. The cluster of skin colours may be approximated using an elliptical shape [
According to equation (
Comparing equations (
FOn the basis of human face identification, it uses characteristic point to make setting of the relative position, applies skin colour ellipse model and CNN of human face identification, and uses Java program to compile skin colour extractor, and its short form is SCE.
Figure
Instruction for SCE operation.
Processing by single image.
Figure
The distribution possibility for input in SCE.
Apply the Taguchi method to get the optimization from the distribution possibility for input in SCE. Figure
The distribution possibility for input in SCE.
Based on the 4 areas defined, Table
A control factor table that may be generated for the colour detection.
Level of control factors | Level | Level 1 | Level 2 | Level 3 | |
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Chin | Radian | A | −1 | 0 | |
Points | B | 3 | 25 | 50 | |
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R-cheek | Radian | C | −1 | 0 | +1 |
Points | D | 3 | 25 | 50 | |
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L-cheek | Radian | E | −1 | 0 | +1 |
Points | F | 3 | 25 | 50 | |
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Forehead | Radian | G | −1 | 0 | +1 |
Points | H | 3 | 25 | 50 |
Factor reaction table.
Exp |
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Ave. |
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1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 98 | 97.5 | 98 | 97.83 | 0.289 | 50.6 |
2 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 94 | 93.8 | 94.2 | 94 | 0.2 | 53.4 |
3 | 1 | 1 | 3 | 3 | 3 | 3 | 3 | 3 | 95 | 94.5 | 96.2 | 95.23 | 0.874 | 40.7 |
4 | 1 | 2 | 1 | 1 | 2 | 2 | 3 | 3 | 96.7 | 96 | 95 | 95.9 | 0.854 | 41 |
5 | 1 | 2 | 2 | 2 | 3 | 3 | 1 | 1 | 94 | 95 | 94 | 94.33 | 0.577 | 44.3 |
6 | 1 | 2 | 3 | 3 | 1 | 1 | 2 | 2 | 94.4 | 94 | 94.5 | 94.3 | 0.265 | 51 |
7 | 1 | 3 | 1 | 2 | 1 | 3 | 2 | 3 | 96.2 | 96 | 96.5 | 96.23 | 0.252 | 51.7 |
8 | 1 | 3 | 2 | 3 | 2 | 1 | 3 | 1 | 96 | 96.5 | 96 | 96.17 | 0.289 | 50.5 |
9 | 1 | 3 | 3 | 1 | 3 | 2 | 1 | 2 | 96 | 96 | 95 | 95.67 | 0.577 | 44.4 |
10 | 2 | 1 | 1 | 3 | 3 | 2 | 2 | 1 | 95.4 | 95.4 | 96.7 | 95.83 | 0.751 | 42.1 |
11 | 2 | 1 | 2 | 1 | 1 | 3 | 3 | 2 | 95 | 94.5 | 95.2 | 94.9 | 0.361 | 48.4 |
12 | 2 | 1 | 3 | 2 | 2 | 1 | 1 | 3 | 96 | 95.1 | 96 | 95.7 | 0.52 | 45.3 |
13 | 2 | 2 | 1 | 2 | 3 | 1 | 3 | 2 | 95.4 | 94 | 96.1 | 95.17 | 1.069 | 39 |
14 | 2 | 2 | 2 | 3 | 1 | 2 | 1 | 3 | 96 | 94 | 95 | 95 | 1 | 39.6 |
15 | 2 | 2 | 3 | 1 | 2 | 3 | 2 | 1 | 95.1 | 96.7 | 96 | 95.93 | 0.802 | 41.6 |
16 | 2 | 3 | 1 | 3 | 2 | 3 | 1 | 2 | 95 | 94.5 | 95.8 | 95.1 | 0.656 | 43.2 |
17 | 2 | 3 | 2 | 1 | 3 | 1 | 2 | 3 | 94.5 | 96.6 | 96.5 | 95.87 | 1.185 | 38.2 |
18 | 2 | 3 | 3 | 2 | 1 | 2 | 3 | 1 | 96.7 | 94.5 | 96 | 95.73 | 1.124 | 38.6 |
From action table, we can clearly see the effect result of quality characteristic because S/N proportion belongs to projection characteristics, and it can easily find the optimized result of every group in the table. Firstly, in the part of Significant in Table
Factor characteristics result table.
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Level 1 | 44.0 | 46.8 | 44.6 | 44.0 | 46.6 | 46.0 | 44.6 | 46.0 |
Level 2 | 41.8 | 42.7 | 45.7 | 42.0 | 45.8 | 43.2 | 44.0 | 44.0 |
Level 3 | 44.4 | 43.6 | 41.0 | 45.0 | 42.0 | 43.0 | 42.7 | |
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−2.2 | −4.0 | 1.1 | −2.0 | −0.8 | −2.8 | −0.6 | −2.0 |
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1.7 | −2.1 | −1.0 | −0.8 | −1.2 | −1.0 | −1.3 | |
Range | 2.2 | 5.7 | 2.2 | 3.0 | 1.6 | 4.0 | 1.6 | 3.3 |
Rank | 5 | 1 | 6 | 4 | 7 | 2 | 8 | 3 |
Significant | No | Yes | No | Yes | No | Yes | No | Yes |
From the analysis result, it is found that importance sequence will change according to the quality characteristic, and it is mainly because the Taguchi method belongs to the optimal method of single quality characteristic and then uses this to program correction base, which can make program of this research quickly calculate the optimized result of skin colour collection.
In the course of the study, it is assumed that the typical image processing software (eg., Photoshop and CorelDraw) is as shown in different steps in Figure
The traditional procedure to capture skin colour.
Six points may have come from part of the hair or shadow, since they are in the range of identification colour values but with different variations of brightness. For debugging efficiently, beside the limited value, the Gaussian distribution concept and standard deviation of the outliers are also removed. Figure
The Gaussian distribution concept and standard deviation.
The procedure of the FaceRGB program is as follows. Calculate Faceskin data. All points of the average distance to FaceLABavg (ΔEavg) and standard deviation Outlier is far from the distance of FaceLABavg (Distanceavg + 2 Refer to equation ( Delete the outlier from the six points
The FaceRGB program is described individually as follows: Open the program, the title indicates FaceRGB. When the file has been read, the image will appear in this picture window; it includes big data read or operation. There is instant synchronization status presenting in the window. Spreadsheet progress strip windows, the situation will progress to the long schedule for a presentation to show they reached results. For big data, create four computation channels in the program, and it will be dealing with huge data in the same time. Figure Option is designed to be read as a single image or input for only one time. This is a single image processing result, including the colour, RGB values, and LAB values. Figure
Processing with four computation channels.
The result of FaceRGB from one image.
This research has created programs to detect the facial colour. They can calculate huge amount of data and even complicated issues by the intelligent method. This colour selecting method can be accumulated for calculating huge data. Therefore, trend for skin colour can be derived from the obtained data. The purpose of this study is to propose a model and procedure for the investigation. Moreover, the process is more important than the result. In addition, the study anticipates that this expert system could be applied into big data type and IOT (internet of things) in the future.
Users will gain their skin colour and the colour location of the face region, which can assist them to select the right colour to match their skin. With it, it will be easier for females to find out their skin colour grouping. Furthermore, after colour harmony and applied aesthetics, every result can be the fashion trend in cosmetics. The expert system can be implemented to develop colour cosmetics; besides, it can be made in the future. Finally, if this system can be applied in the make-up market, it will make a considerable contribution and value.
The optimization data (through the Taguchi Method) used to support the findings of this study are included within the article.
The authors declare that they have no conflicts of interest.
The authors are grateful to The Institute of Minnan Culture Innovation Design & Technology Research, Minnan Normal University, for supporting this research under Grant no. 2018ZDJ03010004.