Skin Type Detection with Deep Learning: A Comparative Analysis

Abstract

There are many factors that can change and affect appearance, including age and environment. Knowing the skin type helps to choose the products best suited to the needs of the skin and therefore the right skin care. Recently, the increasing demand for cosmetics and the scarcity of well-equipped cosmetologists have encouraged cosmetology centers to meet the need by using artificial intelligence applications. Deep learning applications can give high accuracy results in determining the skin type. Recent research shows that learning performs better on nonlinear data than machine learning methods. The aim of this study is to find the best classification model for skin type prediction in skin analysis data with deep learning. For this purpose, 4 different optimization algorithms as Sgd, Adagrad, Adam and Adamax; Tanh and ReLU activation functions and combinations of different neuron numbers using, 16 different models were created.In experimental studies, the performance of the models varies according to the parameters, and it has been observed that the most successful deep neural network model is the model consisting of 64 neurons, Sgd optimization function and ReLU activation function combination with a success rate of 93.75. The accuracy result obtained has a higher classification success compared to other methods, and shows that deep neural networks can make an accurate skin type classification.