AI Article Synopsis
- The classification of human skin types is crucial for fields like dermatology and cosmetology, but traditional subjective methods often lead to unreliable results.
- A new method called the distance-based integration method was introduced, utilizing the Fitzpatrick skin scale and a Fuzzy Analytic Hierarchy Process to objectively determine skin types.
- In a study using 1,022 clinical images, this method achieved impressive results with an average accuracy of 93%, precision of 80%, and specificity of 96%.
Article Abstract
Although identifying human skin types is essential in dermatology, cosmetology, and facial recognition, the classification of human skin types is challenging due to the complex nature, varied characteristics, and the influence of external factors. Traditional methods for skin type identification often rely on subjective assessments, leading to inconsistent and inaccurate results. Therefore, this paper proposes a novel method named a distance-based integration method to identify skin types based on the Fitzpatrick skin scale, also known as the Fitzpatrick skin type. This study focuses on the objective distance measurement, integrated with the Fuzzy Analytic Hierarchy Process (AHP). The objective distance was utilized to determine the distance between each HEX color code for a clinical image and each target skin type. The Fuzzy AHP algorithm was employed to calculate the total score for each target class to identify human skin type. For this study, 1,022 images of human skin were used in the experiment. The results indicated that the proposed method achieved a high average accuracy of 93 %, precision of 80 %, and specificity of 96 %.
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| http://dx.doi.org/10.1016/j.compbiomed.2024.108575 | DOI Listing |
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