Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/12860
ชื่อเรื่อง: Enhancing Lipstick Try-On with Augmented Reality and Color Prediction Model
ผู้แต่ง: Wiwatwattana N.
Chareonnivassakul S.
Maleerutmongkol N.
Charoenvitvorakul C.
Keywords: Augmented reality
Cosmetics
Decision trees
Forecasting
Linear regression
Color prediction model
Detection and tracking
Facial landmark detection
Mobile augmented reality
Multiple linear regressions
Regression
Support vector regression (SVR)
Virtual makeup
Color
วันที่เผยแพร่: 2018
บทคัดย่อ: One of the important tasks in purchasing cosmetics is the selection process. Swatching is the best way in shade-matching the look and feel of cosmetics. However, swatching the lipstick color on skins is far from being a good representation of the lips color. This paper aims to develop a virtual lipstick try-on application based on augmented reality and color prediction model. The goal of the color prediction model is to predict the RGB of the worn lips color given an undertone color of the lips and a lipstick shade. We have studied the performance of several learning models including simple and multiple linear regression, reduced-error pruning decision tree, M5P model tree, support vector regression, stacking technique, and random forests. We find that ensemble methods work best. However, since ensemble methods win only a small margin, our application is implemented with a simpler algorithm that is faster to train and to test, the M5P. The detection and tracking of lips are implemented using the OpenFace toolkit’s facial landmark detection sub-module. Measuring the prediction accuracy with MAE and RMSE, we have demonstrated that our approach that predicts worn lips colors performs better than without the prediction. Lipstick shades that resemble human skins have been shown to give more accurate results than dark shades or light pink shades. © 2018, Springer International Publishing AG, part of Springer Nature.
URI: https://ir.swu.ac.th/jspui/handle/123456789/12860
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045854399&doi=10.1007%2f978-3-319-77028-4_48&partnerID=40&md5=54dd5137697f887ba8f95f226a05eed1
ISSN: 21945357
Appears in Collections:Scopus 1983-2021

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