Abstract
Skin attribute tests, especially for women, have become critical in the development of daily cosmetics in recent years. However, clinical skin attribute testing is often costly and time consuming. In this paper, a novel prediction approach based on questionnaires using recurrent neural network models is proposed for participants’ skin attribute prediction. The prediction engine, which is the most important part of this novel approach, is composed of three prediction models. Each of these models is a neural network allocated to predict different skin attributes: Tone, Spots, and Hydration. We also provide a detailed analysis and solution about the preprocessing of data, the selection of key features, and the evaluation of results. Our prediction system is much faster and more cost effective than traditional clinical skin attribute tests. The system performs very well, and the prediction results show good precision, especially for Tone.
