Human skin type classification using image processing and deep learning approaches

Sirawit Saiwaeo; Sujitra Arwatchananukul; Lapatrada Mungmai; Weeraya Preedalikit; Nattapol Aunsri

doi:10.1016/j.heliyon.2023.e21176

Human skin type classification using image processing and deep learning approaches

Heliyon. 2023 Oct 23;9(11):e21176. doi: 10.1016/j.heliyon.2023.e21176. eCollection 2023 Nov.

Authors

Sirawit Saiwaeo¹, Sujitra Arwatchananukul^{1

2}, Lapatrada Mungmai^{3

4}, Weeraya Preedalikit^{3

4}, Nattapol Aunsri^{1

2}

Affiliations

¹ School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand.
² Integrated AgriTech Ecosystem Research Group (IATE), Mae Fah Luang University, Chiang Rai, Thailand.
³ Division of Cosmetic Science, School of Pharmaceutical Sciences, University of Phayao, Phayao, Thailand.
⁴ Research and Innovation Center in Cosmetic Sciences and Natural products, Division of Cosmetic Sciences, School of Pharmaceutical Sciences, University of Phayao, Phayao, Thailand.

Abstract

Cosmetics consumers need to be aware of their skin type before purchasing products. Identifying skin types can be challenging, especially when they vary from oily to dry in different areas, with skin specialist providing more accurate results. In recent years, artificial intelligence and machine learning have been utilized across various fields, including medicine, to assist in identifying and predicting situations. This study developed a skin type classification model using a Convolutional Neural Networks (CNN) deep learning algorithms. The dataset consisted of normal, oily, and dry skin images, with 112 images for normal skin, 120 images for oily skin, and 97 images for dry skin. Image quality was enhanced using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique, with data augmentation by rotation applied to increase dataset variety, resulting in a total of 1,316 images. CNN architectures including MobileNet-V2, EfficientNet-V2, InceptionV2, and ResNet-V1 were optimized and evaluated. Findings showed that the EfficientNet-V2 architecture performed the best, achieving an accuracy of 91.55% with average loss of 22.74%. To further improve the model, hyperparameter tuning was conducted, resulting in an accuracy of 94.57% and a loss of 13.77%. The Model performance was validated using 10-fold cross-validation and tested on unseen data, achieving an accuracy of 89.70% with a loss of 21.68%.

Keywords: CNN; Contrast limited adaptive histogram equalization (CLAHE); Data preparation; Image augmentation; Image enhancement; Skin images.