Citation

Yeo, Quan Fong and Ooi, Shih Yin and Pang, Ying Han (2025) Advancing Skin Type Classification: The Development of Malaysian Facial Skin Texture Datasets for Machine Learning. In: 15th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2025, 24 May 2025 - 25 May 2025, Penang, Malaysia.

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Abstract

Accurate facial skin type classification is crucial in dermatology and cosmetics, with systems like the Baumann Skin Type System (BSTS) offering a comprehensive framework. However, no publicly available dataset supports facial skin type classification for research purposes. Existing public datasets, such as those for skin diseases, focus on medical conditions and lack the granularity needed for general skin type studies. Attempts to collaborate with dermatological institutions to access proprietary datasets were unsuccessful due to Health Insurance Portability and Accountability Act (HIPAA) regulations and consent issues, further highlighting the resource gap. This paper presents the development of the Malaysian Facial Skin Texture Dataset (MFSTD) in two iterations, focusing on leveraging Few-Shot Learning (FSL) to optimize Baumann Skin Type classification. MFSTD version one (MFSTDv1) provided a balanced dataset under controlled and uncontrolled environments, while MFSTD version two (MFSTDv2) introduced improved diversity, annotations, and preprocessing methods. Initial experiments demonstrated that both MFSTDv1 and MFSTDv2 achieve high classification accuracy, with MFSTDv1 reaching 94.23 ± 3.23% in the 3-way, 10-shot, 5-query scenario and MFSTDv2 achieving 92.51 ± 0.28% in the 3-way, 10-shot, 10-query scenario, showcasing their suitability for machine learning applications, particularly for FSL techniques. These results underline the potential of MFSTD as a valuable resource for advancing facial skin type classification.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Malaysian facial skin texture dataset (MFSTD), baumann skin types
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics R Medicine > RC Internal medicine > RC71-78.7 Examination. Diagnosis
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	17 Mar 2026 05:39
Last Modified:	06 Apr 2026 04:02
URII:	http://shdl.mmu.edu.my/id/eprint/15484

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