Facial Skin Type Classification Based on Microscopic Images Using Convolutional Neural Network (CNN)

Skin is part of the human body that has a function as a barrier from the external environment and gives a physical appearance to an individual. In general, human skin types are classified into normal, dry, oily and combination skin. These skin types affected by the amount and change in facial sebum secretion and hydration ability. Determination of the type of facial skin is needed to determine skin care products and cosmetics in accordance with the type of facial skin they have. In this research, a system design for digital-based facial skin types using Convolutional Neural Network (CNN) method which has advantages to produce features and characteristics from the microscopic images dataset. The primary datasets taken directly using microscopic cameras and have been validated by a dermatologist. The CNN proposed model in this study consists of 3 hidden layers that use 3 × 3 size filters with output channels 8, 16 and 32 respectively, fully connected layer and softmax activation. The proposed model was able to classify the skin types into normal, dry, oily skin conditions and the combination with the best accuracy of 99.5% from 1200 training images and 400 test images used, meanwhile the parameters of recall precision and f-1 score produce values close to 1, which means that it is almost perfect or it can be said that the error is small.