Lightweight Fitzpatrick-scale-based skin tone classification on u-health edge device

The Fitzpatrick scale is a commonly used tool in dermatology to categorize skin types based on melanin and sensitivity to Ultraviolet (UV) light. Existing methodologies for Fitzpatrick scale classification use Individual Typology Angle (ITA) approach for image classification. A primary task is to apply specific filters to detect skin regions in the image. However, such approaches relax their accuracy criteria allowing one tone difference, and the classification accuracy is no more than 75%. In this paper, we present a novel approach that uses specialized filters to detect and remove skin surface attributes, i.e., wrinkles and pores, over a dataset produced in a controlled environment by a lightweight u-health edge device. Image features are modeled as a 3-dimensional feature vector, and we conducted extensive Fitzpatrick classification experiments using Machine Learning (ML) models. The cross-validation outcomes demonstrate improved accuracy, reaching up to 90% while outperforming state-of-the-art methods without relaxing the accuracy criteria.