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.

中文翻译：

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面部肤色检测的智能模型

关于面部颜色的研究很少。例如，化妆品的选择通常着重于时尚或冲动购买。人们从不尝试用面部颜色做出正确的决定。同时，面部颜色也可以是预防健康或疾病的方法。提出了一套基于人脸识别的智能肤色采集方法。首先在面部拍摄彩色照片，然后通过FACE ⁺⁺在图像中实现面部的面部位置设置作为人脸识别结果。而且，它通过人脸的面部特征找出人脸集合的皮肤色点。作者创建了一个SCE程序来收集每张照片的面部颜色，并建立了一个假设，该假设使用最小捕获点假设进行有效计算。其次，通过田口质量改进方法进行假设演示，优化了六点皮肤采集点，并使用平均值计算出面部的代表性肤色。它是通过高斯分布标准差和CIE 2000色差公式完成的，并使用此相关理论构建了优化程序FaceRGB。这项研究可以推广到化妆品采购，并在应用大数据后扩展到面部组分析。智能模型可以快速有效地捕获肤色。这将是未来大数据时尚应用程序的基础工作。