Automatic Measurement of Comprehensive Skin Types Based on Image Processing and Deep Learning

被引:0
|
作者
Ran, Jianghong [1 ,2 ]
Dong, Guolong [1 ,2 ]
Yi, Fan [1 ,2 ]
Li, Li [1 ,2 ]
Wu, Yue [1 ,2 ]
机构
[1] Beijing Technol & Business Univ, Key Lab Cosmet, China Natl Light Ind, Beijing 100048, Peoples R China
[2] Beijing Technol & Business Univ, Inst Cosmet Regulatory Sci, Beijing 100048, Peoples R China
来源
ELECTRONICS | 2025年 / 14卷 / 01期
关键词
Baumann skin type indicator; Inception-v3; fine-tuning; biophysical properties; non-invasive measurement;
D O I
10.3390/electronics14010049
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The skin serves as a physical and chemical barrier, effectively protecting us against the external environment. The Baumann Skin Type Indicator (BSTI) classifies skin into 16 types based on traits such as dry/oily (DO), sensitive/resistant (SR), pigmented/nonpigmented (PN), and wrinkle-prone/tight (WT). Traditional assessments are time-consuming and challenging as they require the involvement of experts. While deep learning has been widely used in skin disease classification, its application in skin type classification, particularly using multimodal data, remains largely unexplored. To address this, we propose an improved Inception-v3 model incorporating transfer learning, based on the four-dimensional classification of the Baumann Skin Type Index (BSTI), which demonstrates outstanding accuracy. The dataset used in this study includes non-invasive physiological indicators, BSTI questionnaires, and skin images captured under various light sources. By comparing performance across different light sources, regions of interest (ROI), and baseline models, the improved Inception-v3 model achieved the best results, with accuracy reaching 91.11% in DO, 81.13% in SR, 91.72% in PN, and 74.9% in WT, demonstrating its effectiveness in skin type classification. This study surpasses traditional classification methods and previous similar research, offering a new, objective approach to measuring comprehensive skin types using multimodal and multi-light-source data.
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收藏
页数:21
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