Facial skin image classification system using Convolutional Neural Networks deep learning algorithm

The global consumption trend of facial skin care products market is gradually changing. With the concept of preventing aging from becoming more common, the age level of using facial skin care products is gradually reduced, so that the demand of young consumer groups gradually increases. This paper used a deep learning algorithm based on the combination of a smart phone and facial skin detection to develop a facial skin image classification system using Convolutional Neural Networks (CNN) deep learning algorithm. In this system, it can recognize three classes facial skin problem, good facial skin quality, bad facial skin quality and face makeup, which helps people quickly understand their facial skin problem. We proposed two different CNN architectures. One has two convolutional layers, two pooling layers and three fully connected layer and the other has three convolution layers, three pooling layers, and four fully connected layer. Finally, we compare the result of our proposed architecture with LeNet-5. From the experimental result, we understand that the architecture which has three convolution layers, three pooling layers, and four fully connected layer, has the highest recognition rate, and we use it as a baseline to build a framework for detecting facial skin problems.