An Intelligent Model for Facial Skin Colour Detection
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2020-01-01
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| Series: | International Journal of Optics |
| Online Access: | http://dx.doi.org/10.1155/2020/1519205 |
| Summary: | 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. |
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| ISSN: | 1687-9384 1687-9392 |