Facial acne recognition system based on machine learning

Abstract

Facial acne plagues many people, causing appearance anxiety and even psychological problems. However, the skin detector or software using traditional image processing technology on the market cannot give consideration to both low cost and high precision. This research aims to develop a low-cost and efficient method to detect facial acne through machine learning. We use hundreds of facial acne patients' pictures collected on the network, use Photoshop to split into thousands of pictures of appropriate size and manually label them as data sets and verification sets, and train them in YOLOX model to finally identify and label skin problems such as facial pustules, acne marks, etc. through one person's facial photos. At present, we have run the system on the desktop (AMD R7 4800H+GTX1650) normally, using the latest YOLOX framework of the open-source YOLO series. In order to improve the learning quality under limited training data, image preprocessing including sharpening and flipping is introduced. The experimental results show that the recognition rate of this method for some skin problems can reach 80%. By further expanding the data set, it can achieve low-cost facial problem recognition. At the same time, this research is also a good case of applying deep learning technology to product design.