FiSC: A Novel Approach for Fitzpatrick Scale-Based Skin Analyzer’s Image Classification

The Fitzpatrick scale is a widely used tool in dermatology for categorizing skin types based on melanin levels and sensitivity to ultraviolet light. The primary objective of this study is to enhance the accuracy of Fitzpatrick scale classification by addressing limitations in existing methodologies. Current approaches either rely on custom-designed hardware or utilize the Individual Typology Angle (ITA) for image classification; however, these methods typically allow for a one-tone difference in classification and achieve a maximum accuracy of approximately 75%. A primary task for skin tone classification in images, is to apply filters to detect skin regions in an image. However, the filters proposed for detecting skin do not apply to general datasets. In this paper, we propose a novel classification method that employs specialized filters to accurately detect and remove skin surface attributes, such as wrinkles and pores, using a controlled environment dataset obtained from a professional skin analyzer device. Our method involves modeling image features as a nine-dimensional feature vector, followed by a dimensionality reduction process to identify the most influential features and dominant areas within the feature space, enabling deployment on low-power devices. We conducted extensive classification experiments using various Machine Learning algorithms. The results of our cross-validation tests demonstrate a significant improvement in classification accuracy, reaching up to 97%, thereby outperforming state-of-the-art methods without relaxing the accuracy criteria.