Natchapol Kittigul
ABSTRACT
About 85% of people between the age of 12 and 24 experience acne, the acne treatment cost exceed $3 billion in U.S.A. Currently dermatologist use manual skin assessment method such as visual and photography then manually mark and count acne on patient face which is time consuming and subjective. This paper proposed acne detection method using Speeded Up Robust Features then classified using 5 designed features: Hue Mean, Standard Deviation (SD) of Red, SD of Green, SD of Blue and Circularity. Quantification using K-Nearest Neighbors algorithm (KNN) was also assessed. The result presented 68% accuracy with 73% sensitivity and 84% precision on average.
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Index Terms
- Acne Detection Using Speeded up Robust Features and Quantification Using K-Nearest Neighbors Algorithm
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