A Novel Deep Neural Network for Facial Beauty Improvement

被引：0

作者：

Ge, Xiao-Xiao ^{[1
]}

Wang, Wen-Feng ^{[2
]}

Patnaik, Lalit Mohan ^{[3
]}

机构：

[1] SILC Business School, Shanghai University, Jiading, Shanghai, China

[2] IEDA Research Institute, Shanghai Institute of Technology, Fengxian, Shanghai, China

[3] National Institute of Advanced Studies, Indian Institute of Science Campus, Karnataka, Bangalore, India

来源：

Journal of Computers (Taiwan) | 2024年 / 35卷 / 01期

关键词：

Computer circuits - Deep neural networks - Learning systems;

D O I：

10.53106/199115992024023501007

中图分类号：

学科分类号：

摘要：

This study delves into how to combine deep learning and fuzzy logic reasoning to evaluate facial aesthetics and provide targeted makeup recommendations. To further optimize the prediction results, we adopted the BLS method to correct the prediction residuals generated by ResNet-50. Specifically, the predicted appearance score can be expressed as score = p + δ, where p is the predicted result and δ represents the predicted residual of the system. After determining the beauty rating, we further studied four different makeup combinations (x1, x2, x3, x4). Moreover, we introduced fuzzy logic reasoning, defined fuzzy sets and fuzzy relationships, and established membership matrices for each makeup combination. The results of these fuzzy logical reasoning allow us to set a value range of m, n for each makeup method. Based on these reasoning results, we have come up with makeup recommendations for different facial aesthetics. Performance our system with the data collected from internet (accuracy of the calculation = 93.26%), from one volunteer (accuracy of the calculation = 98.14%) and from the both with different makeup skills (accuracy of the calculation = 95.63%) demonstrated that the visual sensing problem is feasible and will be a novel direction for the related engineering applications. © 2024 Codon Publications. All rights reserved.

引用

页码：97 / 107