Design and Implementation of Hair Recommendation System Based on Face Recognition
- DOI
- 10.2991/mmsta-19.2019.38How to use a DOI?
- Keywords
- hair style recommendation; face recognition; deep learning; software development
- Abstract
Based on the introduction of face recognition technology and hair style recommendation algorithm, this paper proposes the overall design and implementation of the Hair Recommendation System Based on Face Recognition platform. The software comprehensively considers the hair length, hair volume, face shape and other factors, through the camera to achieve parameter acquisition, face recognition, facial features sampling for hair style recommendations. The software solves the problem that people can't get the expected shape because they can't analyze their hair and facial features correctly when they are getting a haircut, so that people can have a more intuitive visual experience while implementing hair style description. The choice of hair style needs to provide a complete solution for the hair industry marketing and personalized customization services, and help the hair industry to transform and upgrade. Compared with other test-making software, the hair recommendation system introduced in the article incorporates a scoring recommendation algorithm, which can recommend the appropriate hairstyle to the user according to the current aesthetic priority. Therefore, it has new features, new forms and new audiences.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zhuocheng Liu AU - Yi Ji AU - Yuxi Hu AU - Tao Zhan PY - 2019/12 DA - 2019/12 TI - Design and Implementation of Hair Recommendation System Based on Face Recognition BT - Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019) PB - Atlantis Press SP - 180 EP - 183 SN - 2352-538X UR - https://doi.org/10.2991/mmsta-19.2019.38 DO - 10.2991/mmsta-19.2019.38 ID - Liu2019/12 ER -