Research Paper: A Deep Learning-based Aesthetic Surgery Recommendation System

We propose in this paper a deep learning-based recommendation system for aesthetic surgery, composing of a mobile application, and a deep learning model. The deep learning model built based on the dataset of before and after-surgery facial images can estimate the probability of the perfection of some parts of a face. In this study, we focus on the most two popular treatments: rejuvenation treatment and eye double fold surgery. It is assumed that the outcomes of our history surgeries are perfect. Firstly a convolutional auto-encoder is trained by eye images before and after surgery captured from various angles. The trained encoder is utilized to extract learned generic eye features. Secondly, the encoder is further trained by pairs of image samples, captured before and after surgery to predict the probability of perfection, the so-called perfection score. Based on this score, the system would suggest whether some sort of specific aesthetic surgeries should be performed. We preliminarily achieve 88.9% and 93.1% accuracy on rejuvenation treatment and eye double fold surgery, respectively.