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.
引用
收藏
页数:21
相关论文
共 50 条
  • [31] Human skin type classification using image processing and deep learning approaches
    Saiwaeo, Sirawit
    Arwatchananukul, Sujitra
    Mungmai, Lapatrada
    Preedalikit, Weeraya
    Aunsri, Nattapol
    HELIYON, 2023, 9 (11)
  • [32] Early Detection of Melanoma Skin Cancer Using Image Processing and Deep Learning
    Shah, Syed Asif Raza
    Ahmed, Israr
    Mujtaba, Ghulam
    Kim, Moon-Hyun
    Kim, Cheonyong
    Noh, Seo-Young
    ADVANCES IN INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING (IIH-MSP 2021 & FITAT 2021), VOL 2, 2022, 278 : 275 - 284
  • [33] Automatic Measurement Technology of Engine Blade Damage Based on Image Processing
    Li, Xiao-Li
    Chen, Xin-Bo
    Wu, Song-Hua
    Liang, De-Jun
    Huang, Fu-Ming
    Tuijin Jishu/Journal of Propulsion Technology, 2023, 44 (04):
  • [34] Automatic measurement system of tire tread length based on image processing
    School of Chemical Engineering & Environment, Beijing Institute of Technology, Beijing
    100081, China
    Guangxue Jingmi Gongcheng, (497-503):
  • [35] Deep Learning-Based Methods for Automatic Diagnosis of Skin Lesions
    El-Khatib, Hassan
    Popescu, Dan
    Ichim, Loretta
    SENSORS, 2020, 20 (06)
  • [36] Detection of fraud in ginger powder using an automatic sorting system based on image processing technique and deep learning
    Jahanbakhshi, Ahmad
    Abbaspour-Gilandeh, Yousef
    Heidarbeigi, Kobra
    Momeny, Mohammad
    COMPUTERS IN BIOLOGY AND MEDICINE, 2021, 136
  • [37] Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading
    Romero-Oraa, Roberto
    Herrero-Tudela, Maria
    Lopez, Maria I.
    Hornero, Roberto
    Garcia, Maria
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2024, 249
  • [38] Deep learning-based image processing in optical microscopy
    Melanthota, Sindhoora Kaniyala
    Gopal, Dharshini
    Chakrabarti, Shweta
    Kashyap, Anirudh Ameya
    Radhakrishnan, Raghu
    Mazumder, Nirmal
    BIOPHYSICAL REVIEWS, 2022, 14 (02) : 463 - 481
  • [39] Fall Behavior Recognition Based on Deep Learning and Image Processing
    Xu, He
    Shen, Leixian
    Zhang, Qingyun
    Cao, Guoxu
    INTERNATIONAL JOURNAL OF MOBILE COMPUTING AND MULTIMEDIA COMMUNICATIONS, 2018, 9 (04) : 1 - 15
  • [40] An Improved Image Processing Based on Deep Learning Backpropagation Technique
    Gao, Yang
    Tian, Yue
    COMPLEXITY, 2022, 2022