Implementation of Baumann Skin Type Indicator Using Machine Learning

The Baumann Skin Type Indicator (BSTI) is a widely used tool to identify various skin parameters, including dry/oily, pigmented/non-pigmented, sensitive/resistant, and wrinkle/tight. This paper presents a novel and robust machine learning-based approach to implementing BSTI. The InceptionV3 neural network was deployed for the machine learning implementation, resulting in an achieved classification accuracy of 84% for the mentioned skin parameters. The Inception model is the foundation of the work presented, which may be modified for various applications to aid in the implementation of BSTI. This framework can provide objective information to assist in determining a person’s skin type by precisely detecting the classification of skin factors. This new approach to implementing BSTI using machine learning has the potential to make a significant impact in the field of dermatology and related industries.